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Published in: Archives of Dermatological Research 4/2024

01-05-2024 | Skin Cancer | Review

Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes

Authors: Maria Myslicka, Aleksandra Kawala-Sterniuk, Anna Bryniarska, Adam Sudol, Michal Podpora, Rafal Gasz, Radek Martinek, Radana Kahankova Vilimkova, Dominik Vilimek, Mariusz Pelc, Dariusz Mikolajewski

Published in: Archives of Dermatological Research | Issue 4/2024

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Abstract

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.
Literature
2.
go back to reference A X, F W (2019) Towards interpretable skin lesion classification with deep learning models. AMIA Annu Symp Proc. 2019:1246–1255 A X, F W (2019) Towards interpretable skin lesion classification with deep learning models. AMIA Annu Symp Proc. 2019:1246–1255
3.
go back to reference Ahmad B, Jun S, Palade V, You Q, Mao L, Zhongjie M (2021) Improving skin cancer classification using heavy-tailed student t-distribution in generative adversarial networks (ted-gan). Diagnostics 11(11):2147PubMedPubMedCentralCrossRef Ahmad B, Jun S, Palade V, You Q, Mao L, Zhongjie M (2021) Improving skin cancer classification using heavy-tailed student t-distribution in generative adversarial networks (ted-gan). Diagnostics 11(11):2147PubMedPubMedCentralCrossRef
4.
go back to reference Aitekenov S, Gaipov A, Bukasov R (2021) Detection and quantification of proteins in human urine. Talanta 223:121718PubMedCrossRef Aitekenov S, Gaipov A, Bukasov R (2021) Detection and quantification of proteins in human urine. Talanta 223:121718PubMedCrossRef
5.
go back to reference Akinyi OE, Kalambuka AH, Dehayem-Kamadjeu A et al (2022) Evaluation of a peak-free chemometric laser-induced breakdown spectroscopy method for direct rapid cancer detection via trace metal biomarkers in tissue. J Spectrosc 2022 Akinyi OE, Kalambuka AH, Dehayem-Kamadjeu A et al (2022) Evaluation of a peak-free chemometric laser-induced breakdown spectroscopy method for direct rapid cancer detection via trace metal biomarkers in tissue. J Spectrosc 2022
6.
go back to reference Aladhadh S, Alsanea M, Aloraini M, Khan T, Habib S, Islam M (2022) An effective skin cancer classification mechanism via medical vision transformer. Sensors 22(11):4008PubMedPubMedCentralCrossRef Aladhadh S, Alsanea M, Aloraini M, Khan T, Habib S, Islam M (2022) An effective skin cancer classification mechanism via medical vision transformer. Sensors 22(11):4008PubMedPubMedCentralCrossRef
7.
go back to reference Alfano R, Pu Y (2013) Optical biopsy for cancer detection. In: Lasers for Medical Applications. Elsevier, pp 325–367 Alfano R, Pu Y (2013) Optical biopsy for cancer detection. In: Lasers for Medical Applications. Elsevier, pp 325–367
8.
go back to reference Alquran H, Qasmieh IA, Alqudah AM, Alhammouri S, Alawneh E, Abughazaleh A, Hasayen F (2017) The melanoma skin cancer detection and classification using support vector machine. In: 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). IEEE, pp 1–5 Alquran H, Qasmieh IA, Alqudah AM, Alhammouri S, Alawneh E, Abughazaleh A, Hasayen F (2017) The melanoma skin cancer detection and classification using support vector machine. In: 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). IEEE, pp 1–5
9.
go back to reference Anushree U, Shetty S, Kumar R, Bharati S (2022) Adjunctive diagnostic methods for skin cancer detection: a review of electrical impedance-based techniques. Bioelectromagnetics 43(3):193–210PubMedCrossRef Anushree U, Shetty S, Kumar R, Bharati S (2022) Adjunctive diagnostic methods for skin cancer detection: a review of electrical impedance-based techniques. Bioelectromagnetics 43(3):193–210PubMedCrossRef
10.
go back to reference Atak MF, Farabi B, Navarrete-Dechent C, Rubinstein G, Rajadhyaksha M, Jain M (2023) Confocal microscopy for diagnosis and management of cutaneous malignancies: clinical impacts and innovation. Diagnostics 13(5):854PubMedPubMedCentralCrossRef Atak MF, Farabi B, Navarrete-Dechent C, Rubinstein G, Rajadhyaksha M, Jain M (2023) Confocal microscopy for diagnosis and management of cutaneous malignancies: clinical impacts and innovation. Diagnostics 13(5):854PubMedPubMedCentralCrossRef
11.
go back to reference Badano A, Revie C, Casertano A, Cheng W-C, Green P, Kimpe T, Krupinski E, Sisson C, Skrøvseth S, Treanor D, Boynton P, Clunie D, Flynn MJ, Heki T, Hewitt S, Homma H, Masia A, Matsui T, Nagy B, Nishibori M, Penczek J, Schopf T, Yagi Y, Yokoi H (2015) Consistency and standardization of color in medical imaging: a consensus report. J Digit Imaging 28(1):41–52. https://doi.org/10.1007/s10278-014-9721-0CrossRefPubMed Badano A, Revie C, Casertano A, Cheng W-C, Green P, Kimpe T, Krupinski E, Sisson C, Skrøvseth S, Treanor D, Boynton P, Clunie D, Flynn MJ, Heki T, Hewitt S, Homma H, Masia A, Matsui T, Nagy B, Nishibori M, Penczek J, Schopf T, Yagi Y, Yokoi H (2015) Consistency and standardization of color in medical imaging: a consensus report. J Digit Imaging 28(1):41–52. https://​doi.​org/​10.​1007/​s10278-014-9721-0CrossRefPubMed
12.
go back to reference Bălăşescu E, Gheorghe A-C, Moroianu A, Turcu G, Brînzea A, Antohe M, Hodorogea A, Manea L, Balaban M, Andrei R et al (2022) Role of immunohistochemistry in the diagnosis and staging of cutaneous squamous-cell carcinomas. Exp Ther Med 23(6):1–12CrossRef Bălăşescu E, Gheorghe A-C, Moroianu A, Turcu G, Brînzea A, Antohe M, Hodorogea A, Manea L, Balaban M, Andrei R et al (2022) Role of immunohistochemistry in the diagnosis and staging of cutaneous squamous-cell carcinomas. Exp Ther Med 23(6):1–12CrossRef
13.
go back to reference Bann DV, Chaikhoutdinov I, Zhu J, Genevieve A (2019) Satellite and in-transit metastatic disease in melanoma skin cancer: a retrospective review of disease presentation, treatment, and outcomes. Dermatol Surg 45(3):371–380PubMedCrossRef Bann DV, Chaikhoutdinov I, Zhu J, Genevieve A (2019) Satellite and in-transit metastatic disease in melanoma skin cancer: a retrospective review of disease presentation, treatment, and outcomes. Dermatol Surg 45(3):371–380PubMedCrossRef
14.
go back to reference Barton V, Armeson K, Hampras S, Ferris LK, Visvanathan K, Rollison D, Alberg AJ (2017) Nonmelanoma skin cancer and risk of all-cause and cancer-related mortality: a systematic review. Arch Dermatol Res 309(4):243–251PubMedPubMedCentralCrossRef Barton V, Armeson K, Hampras S, Ferris LK, Visvanathan K, Rollison D, Alberg AJ (2017) Nonmelanoma skin cancer and risk of all-cause and cancer-related mortality: a systematic review. Arch Dermatol Res 309(4):243–251PubMedPubMedCentralCrossRef
15.
go back to reference Becker AS, Marcon M, Ghafoor S, Wurnig MC, Frauenfelder T, Boss A (2017) Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest Radiol 52(7):434–440PubMedCrossRef Becker AS, Marcon M, Ghafoor S, Wurnig MC, Frauenfelder T, Boss A (2017) Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest Radiol 52(7):434–440PubMedCrossRef
16.
go back to reference Benjumea E, Morales Y, Torres C, Vilardy J (2019) Characterization of thermographic images of skin cancer lesions using digital image processing. J Phys 1221:012076 (IOP Publishing) Benjumea E, Morales Y, Torres C, Vilardy J (2019) Characterization of thermographic images of skin cancer lesions using digital image processing. J Phys 1221:012076 (IOP Publishing)
17.
go back to reference Brunsgaard E, Wu Y, Grossman D (2022) Melanoma in skin of color: part I. Epidemiology and clinical presentation. J Am Acad Dermatol S0190–9622(22):00783–6 Brunsgaard E, Wu Y, Grossman D (2022) Melanoma in skin of color: part I. Epidemiology and clinical presentation. J Am Acad Dermatol S0190–9622(22):00783–6
18.
go back to reference Brunsgaard E, Jensen J, Grossman D (2022) Melanoma in skin of color: part II. Racial disparities, role of UV and interventions for earlier detection. J Am Acad Dermatol S0190–9622(22):00784–8 Brunsgaard E, Jensen J, Grossman D (2022) Melanoma in skin of color: part II. Racial disparities, role of UV and interventions for earlier detection. J Am Acad Dermatol S0190–9622(22):00784–8
19.
go back to reference Byvaltsev VA, Bardonova LA, Onaka NR, Polkin RA, Ochkal SV, Shepelev VV, Aliyev MA, Potapov AA (2019) Acridine orange: a review of novel applications for surgical cancer imaging and therapy. Front Oncol 9:925PubMedPubMedCentralCrossRef Byvaltsev VA, Bardonova LA, Onaka NR, Polkin RA, Ochkal SV, Shepelev VV, Aliyev MA, Potapov AA (2019) Acridine orange: a review of novel applications for surgical cancer imaging and therapy. Front Oncol 9:925PubMedPubMedCentralCrossRef
20.
go back to reference Calin MA, Parasca SV, Savastru R, Calin MR, Dontu S (2013) Optical techniques for the noninvasive diagnosis of skin cancer. J Cancer Res Clin Oncol 139:1083–1104PubMedCrossRef Calin MA, Parasca SV, Savastru R, Calin MR, Dontu S (2013) Optical techniques for the noninvasive diagnosis of skin cancer. J Cancer Res Clin Oncol 139:1083–1104PubMedCrossRef
21.
go back to reference Chakraborty D, Natarajan C, Mukherjee A (2019) Advances in oral cancer detection. Adv Clin Chem 91:181–200PubMedCrossRef Chakraborty D, Natarajan C, Mukherjee A (2019) Advances in oral cancer detection. Adv Clin Chem 91:181–200PubMedCrossRef
22.
go back to reference Chan S, Reddy V, Myers B, Thibodeaux Q, Brownstone N, Liao W (2020) Machine learning in dermatology: current applications, opportunities, and limitations. Dermatol Ther 10(3):365–386CrossRef Chan S, Reddy V, Myers B, Thibodeaux Q, Brownstone N, Liao W (2020) Machine learning in dermatology: current applications, opportunities, and limitations. Dermatol Ther 10(3):365–386CrossRef
23.
go back to reference Chang W-Y, Huang A, Yang C-Y, Lee C-H, Chen Y-C, Wu T-Y, Gwo-Shing C (2013) Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study. PLoS One 8(11):76212CrossRef Chang W-Y, Huang A, Yang C-Y, Lee C-H, Chen Y-C, Wu T-Y, Gwo-Shing C (2013) Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study. PLoS One 8(11):76212CrossRef
24.
go back to reference Chauhan J, Aasaithambi S, Márquez-Rodas I, Formisano L, Papa S, Meyer N, Forschner A, Faust G, Lau M, Sagkriotis A et al (2022) Understanding the lived experiences of patients with melanoma: real-world evidence generated through a European social media listening analysis. JMIR Cancer 8(2):35930CrossRef Chauhan J, Aasaithambi S, Márquez-Rodas I, Formisano L, Papa S, Meyer N, Forschner A, Faust G, Lau M, Sagkriotis A et al (2022) Understanding the lived experiences of patients with melanoma: real-world evidence generated through a European social media listening analysis. JMIR Cancer 8(2):35930CrossRef
25.
go back to reference Chen Q, Li M, Chen C, Zhou P, Lv X, Chen C (2022) Mdfnet: application of multimodal fusion method based on skin image and clinical data to skin cancer classification. J Cancer Res Clin Oncol 1–13 Chen Q, Li M, Chen C, Zhou P, Lv X, Chen C (2022) Mdfnet: application of multimodal fusion method based on skin image and clinical data to skin cancer classification. J Cancer Res Clin Oncol 1–13
26.
go back to reference Chuang T-Y, Popescu NA, Su W-PD, Chute CG (1990) Squamous cell carcinoma: a population-based incidence study in Rochester, minn. Arch Dermatol 126(2):185–188PubMedCrossRef Chuang T-Y, Popescu NA, Su W-PD, Chute CG (1990) Squamous cell carcinoma: a population-based incidence study in Rochester, minn. Arch Dermatol 126(2):185–188PubMedCrossRef
27.
go back to reference Ciuciulete A, Stepan A, Andreiana B, Simionescu C (2022) Non-melanoma skin cancer: statistical associations between clinical parameters. Curr Health Sci J 48(1):110–115PubMedPubMedCentral Ciuciulete A, Stepan A, Andreiana B, Simionescu C (2022) Non-melanoma skin cancer: statistical associations between clinical parameters. Curr Health Sci J 48(1):110–115PubMedPubMedCentral
28.
go back to reference Crisan D, Kastler S, Scharffetter-Kochanek K, Crisan M, Schneider L-A (2023) Ultrasonographic assessment of depth infiltration in melanoma and non-melanoma skin cancer. J Ultrasound Med Crisan D, Kastler S, Scharffetter-Kochanek K, Crisan M, Schneider L-A (2023) Ultrasonographic assessment of depth infiltration in melanoma and non-melanoma skin cancer. J Ultrasound Med
29.
go back to reference Dai X, Spasić I, Meyer B, Chapman S, Andres F (2019) Machine learning on mobile: An on-device inference app for skin cancer detection. In: 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, pp 301–305 Dai X, Spasić I, Meyer B, Chapman S, Andres F (2019) Machine learning on mobile: An on-device inference app for skin cancer detection. In: 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, pp 301–305
30.
go back to reference Das K, Cockerell CJ, Patil A, Pietkiewicz P, Giulini M, Grabbe S, Goldust M (2021) Machine learning and its application in skin cancer. Int J Environ Res Public Health 18(24):13409PubMedPubMedCentralCrossRef Das K, Cockerell CJ, Patil A, Pietkiewicz P, Giulini M, Grabbe S, Goldust M (2021) Machine learning and its application in skin cancer. Int J Environ Res Public Health 18(24):13409PubMedPubMedCentralCrossRef
33.
go back to reference De Rosa FS, Bentley MVL (2000) Photodynamic therapy of skin cancers: sensitizers, clinical studies and future directives. Pharm Res 17:1447–1455PubMedCrossRef De Rosa FS, Bentley MVL (2000) Photodynamic therapy of skin cancers: sensitizers, clinical studies and future directives. Pharm Res 17:1447–1455PubMedCrossRef
34.
go back to reference Del Marmol V (2022) Prevention and screening of melanoma in Europe: 20 years of the euromelanoma campaign. J Eur Acad Dermatol Venereol 36(Suppl 6):5–11PubMedCrossRef Del Marmol V (2022) Prevention and screening of melanoma in Europe: 20 years of the euromelanoma campaign. J Eur Acad Dermatol Venereol 36(Suppl 6):5–11PubMedCrossRef
35.
go back to reference Dengel LT, Petroni GR, Judge J, Chen D, Acton ST, Schroen AT, Slingluff CL Jr (2015) Total body photography for skin cancer screening. Int J Dermatol 54(11):1250–1254PubMedCrossRef Dengel LT, Petroni GR, Judge J, Chen D, Acton ST, Schroen AT, Slingluff CL Jr (2015) Total body photography for skin cancer screening. Int J Dermatol 54(11):1250–1254PubMedCrossRef
36.
go back to reference Didona D, Paolino G, Bottoni U, Cantisani C (2018) Non melanoma skin cancer pathogenesis overview. Biomedicines 6(1) Didona D, Paolino G, Bottoni U, Cantisani C (2018) Non melanoma skin cancer pathogenesis overview. Biomedicines 6(1)
37.
go back to reference Dika E, Curti N, Giampieri E, Veronesi G, Misciali C, Ricci C, Castellani G, Patrizi A, Marcelli E (2022) Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: a pilot study. Pathol-Res Pract 154014 Dika E, Curti N, Giampieri E, Veronesi G, Misciali C, Ricci C, Castellani G, Patrizi A, Marcelli E (2022) Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: a pilot study. Pathol-Res Pract 154014
38.
go back to reference Dinnes J, Deeks JJ, Chuchu N, Matin RN, Wong KY, Aldridge RB, Durack A, Gulati A, Chan SA, Johnston L et al (2018) Visual inspection and dermoscopy, alone or in combination, for diagnosing keratinocyte skin cancers in adults. Cochrane Database Syst Rev (12) Dinnes J, Deeks JJ, Chuchu N, Matin RN, Wong KY, Aldridge RB, Durack A, Gulati A, Chan SA, Johnston L et al (2018) Visual inspection and dermoscopy, alone or in combination, for diagnosing keratinocyte skin cancers in adults. Cochrane Database Syst Rev (12)
39.
go back to reference Dobre E, Constantin C, M N (2022) Skin cancer research goes digital: looking for biomarkers within the droplets. J Pers Med 12(7):1136 Dobre E, Constantin C, M N (2022) Skin cancer research goes digital: looking for biomarkers within the droplets. J Pers Med 12(7):1136
41.
go back to reference El-Khalawany M, Hassab-El-Naby HM, Mousa AM, Sameh A, Rageh MA, Genedy RM, Hosny AM, Aboelmagd MA, Aboeldahab S (2022) Epidemiological and clinicopathological analysis of basal cell carcinoma in Egyptian population: a 5-year retrospective multicenter study. J Cancer Res Clin Oncol 1–9 El-Khalawany M, Hassab-El-Naby HM, Mousa AM, Sameh A, Rageh MA, Genedy RM, Hosny AM, Aboelmagd MA, Aboeldahab S (2022) Epidemiological and clinicopathological analysis of basal cell carcinoma in Egyptian population: a 5-year retrospective multicenter study. J Cancer Res Clin Oncol 1–9
42.
go back to reference El-Shenawee M, Vohra N, Bowman T, Bailey K (2019) Cancer detection in excised breast tumors using terahertz imaging and spectroscopy. Biomed Spectrosc Imaging 8(1–2):1–9PubMedPubMedCentralCrossRef El-Shenawee M, Vohra N, Bowman T, Bailey K (2019) Cancer detection in excised breast tumors using terahertz imaging and spectroscopy. Biomed Spectrosc Imaging 8(1–2):1–9PubMedPubMedCentralCrossRef
43.
go back to reference Fania L, Didona D, Morese R, Campana I, Coco V, Di Pietro FR, Ricci F, Pallotta S, Candi E, Abeni D et al (2020) Basal cell carcinoma: from pathophysiology to novel therapeutic approaches. Biomedicines 8(11):449PubMedPubMedCentralCrossRef Fania L, Didona D, Morese R, Campana I, Coco V, Di Pietro FR, Ricci F, Pallotta S, Candi E, Abeni D et al (2020) Basal cell carcinoma: from pathophysiology to novel therapeutic approaches. Biomedicines 8(11):449PubMedPubMedCentralCrossRef
44.
go back to reference Farooq MA, Corcoran P (2020) Infrared imaging for human thermography and breast tumor classification using thermal images. In: 2020 31st Irish Signals and Systems Conference (ISSC). IEEE, pp 1–6 Farooq MA, Corcoran P (2020) Infrared imaging for human thermography and breast tumor classification using thermal images. In: 2020 31st Irish Signals and Systems Conference (ISSC). IEEE, pp 1–6
45.
go back to reference Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Piñeros M, Znaor A, Bray F (2021) Cancer statistics for the year 2020: an overview. Int J Cancer 149(4):778–789CrossRef Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Piñeros M, Znaor A, Bray F (2021) Cancer statistics for the year 2020: an overview. Int J Cancer 149(4):778–789CrossRef
46.
47.
go back to reference Fried LJ, Tan A, Berry EG, Braun RP, Curiel-Lewandrowski C, Curtis J, Ferris LK, Hartman RI, Jaimes N, Kawaoka JC et al (2021) Dermoscopy proficiency expectations for us dermatology resident physicians: results of a modified Delphi survey of pigmented lesion experts. JAMA Dermatol 157(2):189–197PubMedCrossRef Fried LJ, Tan A, Berry EG, Braun RP, Curiel-Lewandrowski C, Curtis J, Ferris LK, Hartman RI, Jaimes N, Kawaoka JC et al (2021) Dermoscopy proficiency expectations for us dermatology resident physicians: results of a modified Delphi survey of pigmented lesion experts. JAMA Dermatol 157(2):189–197PubMedCrossRef
48.
go back to reference Fujimoto JG, Pitris C, Boppart SA, Brezinski ME (2000) Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy. Neoplasia 2(1–2):9–25PubMedPubMedCentralCrossRef Fujimoto JG, Pitris C, Boppart SA, Brezinski ME (2000) Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy. Neoplasia 2(1–2):9–25PubMedPubMedCentralCrossRef
49.
go back to reference Garnavi R, Aldeen M, Bailey J (2012) Computer-aided diagnosis of melanoma using border-and wavelet-based texture analysis. IEEE Trans Inf Technol Biomed 16(6):1239–1252PubMedCrossRef Garnavi R, Aldeen M, Bailey J (2012) Computer-aided diagnosis of melanoma using border-and wavelet-based texture analysis. IEEE Trans Inf Technol Biomed 16(6):1239–1252PubMedCrossRef
50.
go back to reference Giavina Bianchi M, Santos A, Cordioli E (2021) Dermatologists’ perceptions on the utility and limitations of teledermatology after examining 55,000 lesions. J Telemed Telecare 27(3):166–173PubMedCrossRef Giavina Bianchi M, Santos A, Cordioli E (2021) Dermatologists’ perceptions on the utility and limitations of teledermatology after examining 55,000 lesions. J Telemed Telecare 27(3):166–173PubMedCrossRef
51.
52.
go back to reference Gouda W, Sama N, Al-Waakid G, Humayun M, Jhanjhi N (2022) Detection of skin cancer based on skin lesion images using deep learning. Healthcare 10(7):1183PubMedPubMedCentralCrossRef Gouda W, Sama N, Al-Waakid G, Humayun M, Jhanjhi N (2022) Detection of skin cancer based on skin lesion images using deep learning. Healthcare 10(7):1183PubMedPubMedCentralCrossRef
54.
go back to reference Griffin LL, Ali FR, Lear JT (2016) Non-melanoma skin cancer. Clin Med 16(1):62CrossRef Griffin LL, Ali FR, Lear JT (2016) Non-melanoma skin cancer. Clin Med 16(1):62CrossRef
55.
go back to reference Guerra K, Urban K, Crane J (2021) Sunburn.[updated aug. 4, 2021]. StatPearls Guerra K, Urban K, Crane J (2021) Sunburn.[updated aug. 4, 2021]. StatPearls
56.
go back to reference Guy GP Jr, Machlin SR, Ekwueme DU, Yabroff KR (2015) Prevalence and costs of skin cancer treatment in the us, 2002–2006 and 2007–2011. Am J Prev Med 48(2):183–187PubMedCrossRef Guy GP Jr, Machlin SR, Ekwueme DU, Yabroff KR (2015) Prevalence and costs of skin cancer treatment in the us, 2002–2006 and 2007–2011. Am J Prev Med 48(2):183–187PubMedCrossRef
57.
go back to reference Guy GP Jr, Thomas CC, Thompson T, Watson M, Massetti GM, Richardson LC (2015) Vital signs: melanoma incidence and mortality trends and projections-united states, 1982–2030. MMWR Morb Mortal Wkly Rep 64(21):591PubMedPubMedCentral Guy GP Jr, Thomas CC, Thompson T, Watson M, Massetti GM, Richardson LC (2015) Vital signs: melanoma incidence and mortality trends and projections-united states, 1982–2030. MMWR Morb Mortal Wkly Rep 64(21):591PubMedPubMedCentral
58.
go back to reference Hagen N, Kudenov MW (2013) Review of snapshot spectral imaging technologies. Opt Eng 52(9):090901–090901CrossRef Hagen N, Kudenov MW (2013) Review of snapshot spectral imaging technologies. Opt Eng 52(9):090901–090901CrossRef
59.
go back to reference Halicek M, Fabelo H, Ortega S, Callico GM, Fei B (2019) In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: revealing the invisible features of cancer. Cancers 11(6):756PubMedPubMedCentralCrossRef Halicek M, Fabelo H, Ortega S, Callico GM, Fei B (2019) In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: revealing the invisible features of cancer. Cancers 11(6):756PubMedPubMedCentralCrossRef
60.
go back to reference Hall E, Fernandez-Lopez E, Silk A, Dummer R, Bhatia S (2020) Immunologic characteristics of nonmelanoma skin cancers: implications for immunotherapy. Am Soc Clin Oncol Educ Book 40:1–10PubMed Hall E, Fernandez-Lopez E, Silk A, Dummer R, Bhatia S (2020) Immunologic characteristics of nonmelanoma skin cancers: implications for immunotherapy. Am Soc Clin Oncol Educ Book 40:1–10PubMed
61.
go back to reference Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, Ohnishi T, Fujishiro M, Matsuo K, Fujisaki J et al (2018) Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21(4):653–660PubMedCrossRef Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, Ohnishi T, Fujishiro M, Matsuo K, Fujisaki J et al (2018) Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21(4):653–660PubMedCrossRef
62.
go back to reference Höhn J, Hekler A, Krieghoff-Henning E, Kather JN, Utikal JS, Meier F, Gellrich FF, Hauschild A, French L, Schlager JG et al (2021) Integrating patient data into skin cancer classification using convolutional neural networks: systematic review. J Med Internet Res 23(7):20708CrossRef Höhn J, Hekler A, Krieghoff-Henning E, Kather JN, Utikal JS, Meier F, Gellrich FF, Hauschild A, French L, Schlager JG et al (2021) Integrating patient data into skin cancer classification using convolutional neural networks: systematic review. J Med Internet Res 23(7):20708CrossRef
63.
go back to reference Housman TS, Feldman SR, Williford PM, Fleischer AB Jr, Goldman ND, Acostamadiedo JM, Chen GJ (2003) Skin cancer is among the most costly of all cancers to treat for the medicare population. J Am Acad Dermatol 48(3):425–429PubMedCrossRef Housman TS, Feldman SR, Williford PM, Fleischer AB Jr, Goldman ND, Acostamadiedo JM, Chen GJ (2003) Skin cancer is among the most costly of all cancers to treat for the medicare population. J Am Acad Dermatol 48(3):425–429PubMedCrossRef
64.
go back to reference Hu W, Fang L, Ni R, Zhang H, Pan G (2022) Changing trends in the disease burden of non-melanoma skin cancer globally from 1990 to 2019 and its predicted level in 25 years. BMC Cancer 22(1):1–11CrossRef Hu W, Fang L, Ni R, Zhang H, Pan G (2022) Changing trends in the disease burden of non-melanoma skin cancer globally from 1990 to 2019 and its predicted level in 25 years. BMC Cancer 22(1):1–11CrossRef
65.
go back to reference I M, CN D (2009) Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inf Technol Biomed. 13(5):721–33 I M, CN D (2009) Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inf Technol Biomed. 13(5):721–33
67.
go back to reference Jain S, Pise N et al (2015) Computer aided melanoma skin cancer detection using image processing. Procedia Comput Sci 48:735–740CrossRef Jain S, Pise N et al (2015) Computer aided melanoma skin cancer detection using image processing. Procedia Comput Sci 48:735–740CrossRef
68.
go back to reference Jaleel JA, Salim S, Aswin R (2013) Computer aided detection of skin cancer. In: 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT). IEEE, pp 1137–1142 Jaleel JA, Salim S, Aswin R (2013) Computer aided detection of skin cancer. In: 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT). IEEE, pp 1137–1142
69.
go back to reference Jaleel JA, Salim S, Aswin R et al (2012) Artificial neural network based detection of skin cancer. Int J Adv Res Electric Electron Instrum Eng 1(3) Jaleel JA, Salim S, Aswin R et al (2012) Artificial neural network based detection of skin cancer. Int J Adv Res Electric Electron Instrum Eng 1(3)
70.
go back to reference Jeng M-J, Sharma M, Sharma L, Chao T-Y, Huang S-F, Chang L-B, Wu S-L, Chow L (2019) Raman spectroscopy analysis for optical diagnosis of oral cancer detection. J Clin Med 8(9):1313PubMedPubMedCentralCrossRef Jeng M-J, Sharma M, Sharma L, Chao T-Y, Huang S-F, Chang L-B, Wu S-L, Chow L (2019) Raman spectroscopy analysis for optical diagnosis of oral cancer detection. J Clin Med 8(9):1313PubMedPubMedCentralCrossRef
71.
go back to reference Jones O, Calanzani N, Saji S, Duffy S, Emery J, Hamilton W, Singh H, Wit N, Walter F (2021) Artificial intelligence techniques that may be applied to primary care data to facilitate earlier diagnosis of cancer: Systematic review. J Med Internet Res 23(3):23483CrossRef Jones O, Calanzani N, Saji S, Duffy S, Emery J, Hamilton W, Singh H, Wit N, Walter F (2021) Artificial intelligence techniques that may be applied to primary care data to facilitate earlier diagnosis of cancer: Systematic review. J Med Internet Res 23(3):23483CrossRef
72.
go back to reference Jones O, Matin R, Schaar M, Prathivadi Bhayankaram K, Ranmuthu C, Islam M, Behiyat D, Boscott R, Calanzani N, Emery J, Williams H, Walter F (2022) Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. Lancet Digit Health 4(6):466–476CrossRef Jones O, Matin R, Schaar M, Prathivadi Bhayankaram K, Ranmuthu C, Islam M, Behiyat D, Boscott R, Calanzani N, Emery J, Williams H, Walter F (2022) Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. Lancet Digit Health 4(6):466–476CrossRef
73.
go back to reference Jussupow E, Spohrer K, A H (2022) Identity threats as a reason for resistance to artificial intelligence: Survey study with medical students and professionals. JMIR Form Res. 6(3):28750 Jussupow E, Spohrer K, A H (2022) Identity threats as a reason for resistance to artificial intelligence: Survey study with medical students and professionals. JMIR Form Res. 6(3):28750
74.
go back to reference Kallipolitis A, Stratigos A, Zarras A, Maglogiannis I (2020) Fully connected visual words for the classification of skin cancer confocal images. In: VISIGRAPP (5: VISAPP), pp 853–858 Kallipolitis A, Stratigos A, Zarras A, Maglogiannis I (2020) Fully connected visual words for the classification of skin cancer confocal images. In: VISIGRAPP (5: VISAPP), pp 853–858
75.
go back to reference Khan N, Mir M, Qian L, Baloch M, Ali Khan M, Rehman A, Ngowi E, Wu D, Ji X (2021) Skin cancer biology and barriers to treatment: recent applications of polymeric micro/nanostructures. J Adv Res 36:223–247PubMedPubMedCentralCrossRef Khan N, Mir M, Qian L, Baloch M, Ali Khan M, Rehman A, Ngowi E, Wu D, Ji X (2021) Skin cancer biology and barriers to treatment: recent applications of polymeric micro/nanostructures. J Adv Res 36:223–247PubMedPubMedCentralCrossRef
76.
go back to reference Khan Z, Shubham T, Arya RK (2022) Skin cancer detection using computer vision. In: Topical Drifts in Intelligent Computing: Proceedings of International Conference on Computational Techniques and Applications (ICCTA 2021). Springer, pp 3–11 Khan Z, Shubham T, Arya RK (2022) Skin cancer detection using computer vision. In: Topical Drifts in Intelligent Computing: Proceedings of International Conference on Computational Techniques and Applications (ICCTA 2021). Springer, pp 3–11
77.
go back to reference Kim S, Kim TG, Lee SH, Kim W, Bang A, Moon SW, Song J, Shin J-H, Yu JS, Choi S (2020) Label-free surface-enhanced Raman spectroscopy biosensor for on-site breast cancer detection using human tears. ACS Appl Mater Interfaces 12(7):7897–7904PubMedCrossRef Kim S, Kim TG, Lee SH, Kim W, Bang A, Moon SW, Song J, Shin J-H, Yu JS, Choi S (2020) Label-free surface-enhanced Raman spectroscopy biosensor for on-site breast cancer detection using human tears. ACS Appl Mater Interfaces 12(7):7897–7904PubMedCrossRef
78.
go back to reference Kim JA, Wales DJ, Yang G-Z (2020) Optical spectroscopy for in vivo medical diagnosis-a review of the state of the art and future perspectives. Progress Biomed Eng 2(4):042001CrossRef Kim JA, Wales DJ, Yang G-Z (2020) Optical spectroscopy for in vivo medical diagnosis-a review of the state of the art and future perspectives. Progress Biomed Eng 2(4):042001CrossRef
79.
go back to reference Kim H-Y, Jung H, Kim H-M, Jeong H-J (2021) Surfactin exerts an anti-cancer effect through inducing allergic reactions in melanoma skin cancer. Int Immunopharmacol 99:107934PubMedCrossRef Kim H-Y, Jung H, Kim H-M, Jeong H-J (2021) Surfactin exerts an anti-cancer effect through inducing allergic reactions in melanoma skin cancer. Int Immunopharmacol 99:107934PubMedCrossRef
80.
go back to reference König K (2020) Clinical in vivo multiphoton flim tomography. Methods Appl Fluorescence 8(3):034002CrossRef König K (2020) Clinical in vivo multiphoton flim tomography. Methods Appl Fluorescence 8(3):034002CrossRef
81.
go back to reference Kothari R, Fong Y, Storrie-Lombardi MC (2020) Review of laser Raman spectroscopy for surgical breast cancer detection: stochastic backpropagation neural networks. Sensors 20(21):6260PubMedPubMedCentralCrossRef Kothari R, Fong Y, Storrie-Lombardi MC (2020) Review of laser Raman spectroscopy for surgical breast cancer detection: stochastic backpropagation neural networks. Sensors 20(21):6260PubMedPubMedCentralCrossRef
82.
go back to reference Lazzari G, Vinciguerra D, Balasso A, Nicolas V, Goudin N, Garfa-Traore M, Fehér A, Dinnyes A, Nicolas J, Couvreur P et al (2019) Light sheet fluorescence microscopy versus confocal microscopy: in quest of a suitable tool to assess drug and nanomedicine penetration into multicellular tumor spheroids. Eur J Pharm Biopharm 142:195–203PubMedCrossRef Lazzari G, Vinciguerra D, Balasso A, Nicolas V, Goudin N, Garfa-Traore M, Fehér A, Dinnyes A, Nicolas J, Couvreur P et al (2019) Light sheet fluorescence microscopy versus confocal microscopy: in quest of a suitable tool to assess drug and nanomedicine penetration into multicellular tumor spheroids. Eur J Pharm Biopharm 142:195–203PubMedCrossRef
83.
go back to reference Leiter U, Eigentler T, Garbe C (2014) Epidemiology of skin cancer. Sunlight, vitamin D and skin cancer 120–140 Leiter U, Eigentler T, Garbe C (2014) Epidemiology of skin cancer. Sunlight, vitamin D and skin cancer 120–140
84.
go back to reference Leiter U, Keim U, Garbe C (2020) Epidemiology of skin cancer: update 2019. In: Sunlight, Vitamin D and Skin Cancer. Springer, pp 123–139 Leiter U, Keim U, Garbe C (2020) Epidemiology of skin cancer: update 2019. In: Sunlight, Vitamin D and Skin Cancer. Springer, pp 123–139
85.
go back to reference Leon R, Martinez-Vega B, Fabelo H, Ortega S, Melian V, Castaño I, Carretero G, Almeida P, Garcia A, Quevedo E et al (2020) Non-invasive skin cancer diagnosis using hyperspectral imaging for in-situ clinical support. J Clin Med 9(6):1662PubMedPubMedCentralCrossRef Leon R, Martinez-Vega B, Fabelo H, Ortega S, Melian V, Castaño I, Carretero G, Almeida P, Garcia A, Quevedo E et al (2020) Non-invasive skin cancer diagnosis using hyperspectral imaging for in-situ clinical support. J Clin Med 9(6):1662PubMedPubMedCentralCrossRef
86.
go back to reference Lin H, Wei C, Wang G, Chen H, Lin L, Ni M, Chen J, Zhuo S (2019) Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning. J Biophotonics 12(7):201800435CrossRef Lin H, Wei C, Wang G, Chen H, Lin L, Ni M, Chen J, Zhuo S (2019) Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning. J Biophotonics 12(7):201800435CrossRef
87.
go back to reference Liu Z, Li T, Wang Z, Liu J, Huang S, Min BH, An JY, Kim KM, Kim S, Chen Y et al (2022) Gold nanopyramid arrays for non-invasive surface-enhanced Raman spectroscopy-based gastric cancer detection via sevs. ACS Appl Nano Mater 5(9):12506–12517PubMedPubMedCentralCrossRef Liu Z, Li T, Wang Z, Liu J, Huang S, Min BH, An JY, Kim KM, Kim S, Chen Y et al (2022) Gold nanopyramid arrays for non-invasive surface-enhanced Raman spectroscopy-based gastric cancer detection via sevs. ACS Appl Nano Mater 5(9):12506–12517PubMedPubMedCentralCrossRef
88.
go back to reference Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer, pp 21–37 Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer, pp 21–37
89.
go back to reference Lopera DOG, Picot F, Shams R, Dallaire F, Sheehy G, Alley S, Barkati M, Delouya G, Carrier J-F, Birlea M et al (2022) Image-guided Raman spectroscopy navigation system to improve Transperineal prostate cancer detection. Part 2: In-vivo tumor-targeting using a classification model combining spectral and mri-radiomics features. J Biomed Opt 27(9):095004 Lopera DOG, Picot F, Shams R, Dallaire F, Sheehy G, Alley S, Barkati M, Delouya G, Carrier J-F, Birlea M et al (2022) Image-guided Raman spectroscopy navigation system to improve Transperineal prostate cancer detection. Part 2: In-vivo tumor-targeting using a classification model combining spectral and mri-radiomics features. J Biomed Opt 27(9):095004
90.
go back to reference Magalhaes C, Vardasca R, Mendes J (2018) Recent use of medical infrared thermography in skin neoplasms. Skin Res Technol 24(4):587–591PubMedCrossRef Magalhaes C, Vardasca R, Mendes J (2018) Recent use of medical infrared thermography in skin neoplasms. Skin Res Technol 24(4):587–591PubMedCrossRef
91.
go back to reference Magalhaes C, Tavares JMR, Mendes J, Vardasca R (2021) Comparison of machine learning strategies for infrared thermography of skin cancer. Biomed Signal Process Control 69:102872CrossRef Magalhaes C, Tavares JMR, Mendes J, Vardasca R (2021) Comparison of machine learning strategies for infrared thermography of skin cancer. Biomed Signal Process Control 69:102872CrossRef
92.
go back to reference Mannaerts CK, Wildeboer RR, Remmers S, Kollenburg RA, Kajtazovic A, Hagemann J, Postema AW, Sloun RJ, Roobol MJ, Tilki D et al (2019) Multiparametric ultrasound for prostate cancer detection and localization: correlation of b-mode, shear wave elastography and contrast enhanced ultrasound with radical prostatectomy specimens. J Urol 202(6):1166–1173PubMedCrossRef Mannaerts CK, Wildeboer RR, Remmers S, Kollenburg RA, Kajtazovic A, Hagemann J, Postema AW, Sloun RJ, Roobol MJ, Tilki D et al (2019) Multiparametric ultrasound for prostate cancer detection and localization: correlation of b-mode, shear wave elastography and contrast enhanced ultrasound with radical prostatectomy specimens. J Urol 202(6):1166–1173PubMedCrossRef
93.
go back to reference Marks R (1995) The epidemiology of non-melanoma skin cancer: who, why and what can we do about it. J Dermatol 22(11):853–857PubMedCrossRef Marks R (1995) The epidemiology of non-melanoma skin cancer: who, why and what can we do about it. J Dermatol 22(11):853–857PubMedCrossRef
94.
go back to reference McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577(7788):89–94PubMedCrossRef McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577(7788):89–94PubMedCrossRef
95.
go back to reference M F, E F (2022) On the automatic detection and classification of skin cancer using deep transfer learning. MDPI 22:4963 M F, E F (2022) On the automatic detection and classification of skin cancer using deep transfer learning. MDPI 22:4963
96.
go back to reference Melsitov O, Sherendak V, Konovalov S, Myakinin O (2018) Automatic malignant melanoma recognition using a dermatoscopy imaging tool. IIB 70:57 Melsitov O, Sherendak V, Konovalov S, Myakinin O (2018) Automatic malignant melanoma recognition using a dermatoscopy imaging tool. IIB 70:57
97.
go back to reference Mendez BM, Thornton JF (2018) Current basal and squamous cell skin cancer management. Plast Reconstr Surg 142(3):373–387CrossRef Mendez BM, Thornton JF (2018) Current basal and squamous cell skin cancer management. Plast Reconstr Surg 142(3):373–387CrossRef
98.
go back to reference Meshram AA, Gade A, Dutonde A (2022) A review of skin melanoma detection based on machine learning. Int J New Pract Manage Eng 11(01):15–23 Meshram AA, Gade A, Dutonde A (2022) A review of skin melanoma detection based on machine learning. Int J New Pract Manage Eng 11(01):15–23
99.
go back to reference Miller DD, Brown EW (2018) Artificial intelligence in medical practice: the question to the answer? Am J Med 131(2):129–133PubMedCrossRef Miller DD, Brown EW (2018) Artificial intelligence in medical practice: the question to the answer? Am J Med 131(2):129–133PubMedCrossRef
100.
go back to reference Moskal P, Dulski K, Chug N, Curceanu C, Czerwiński E, Dadgar M, Gajewski J, Gajos A, Grudzień G, Hiesmayr BC et al (2021) Positronium imaging with the novel multiphoton pet scanner. Sci Adv 7(42):4394CrossRef Moskal P, Dulski K, Chug N, Curceanu C, Czerwiński E, Dadgar M, Gajewski J, Gajos A, Grudzień G, Hiesmayr BC et al (2021) Positronium imaging with the novel multiphoton pet scanner. Sci Adv 7(42):4394CrossRef
101.
go back to reference M G, T K, S Y, S H (2020) Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities, vol 127. Elsevier, p 104065 M G, T K, S Y, S H (2020) Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities, vol 127. Elsevier, p 104065
102.
go back to reference Mukundan A, Huang C-C, Men T-C, Lin F-C, Wang H-C (2022) Air pollution detection using a novel snap-shot hyperspectral imaging technique. Sensors 22(16):6231PubMedPubMedCentralCrossRef Mukundan A, Huang C-C, Men T-C, Lin F-C, Wang H-C (2022) Air pollution detection using a novel snap-shot hyperspectral imaging technique. Sensors 22(16):6231PubMedPubMedCentralCrossRef
103.
go back to reference Narayanamurthy V, Padmapriya P, Noorasafrin A, Pooja B, Hema K, Nithyakalyani K, Samsuri F et al (2018) Skin cancer detection using non-invasive techniques. RSC Adv 8(49):28095–28130PubMedPubMedCentralCrossRef Narayanamurthy V, Padmapriya P, Noorasafrin A, Pooja B, Hema K, Nithyakalyani K, Samsuri F et al (2018) Skin cancer detection using non-invasive techniques. RSC Adv 8(49):28095–28130PubMedPubMedCentralCrossRef
104.
go back to reference Narayanan D, Saladi R, Fox J (2010) Ultraviolet radiation and skin cancer. Int J Dermatol 49(9):978–986PubMedCrossRef Narayanan D, Saladi R, Fox J (2010) Ultraviolet radiation and skin cancer. Int J Dermatol 49(9):978–986PubMedCrossRef
105.
go back to reference Navarrete-Dechent C, Liopyris K, Marchetti MA (2021) Multiclass artificial intelligence in dermatology-progress but still room for improvement. J Invest Dermatol 141(5):1325PubMedCrossRef Navarrete-Dechent C, Liopyris K, Marchetti MA (2021) Multiclass artificial intelligence in dermatology-progress but still room for improvement. J Invest Dermatol 141(5):1325PubMedCrossRef
106.
go back to reference Ng EY, Etehadtavakol M (2017) Application of infrared to biomedical sciences. Springer, BerlinCrossRef Ng EY, Etehadtavakol M (2017) Application of infrared to biomedical sciences. Springer, BerlinCrossRef
107.
go back to reference Nogueira MS, Maryam S, Amissah M, Lynch N, Killeen S, O’Riordain M, Andersson-Engels S (2021) Benefit of extending near-infrared wavelength range of diffuse reflectance spectroscopy for colorectal cancer detection using machine learning. In: European Conference on Biomedical Optics. Optica Publishing Group, pp 4–16 Nogueira MS, Maryam S, Amissah M, Lynch N, Killeen S, O’Riordain M, Andersson-Engels S (2021) Benefit of extending near-infrared wavelength range of diffuse reflectance spectroscopy for colorectal cancer detection using machine learning. In: European Conference on Biomedical Optics. Optica Publishing Group, pp 4–16
109.
go back to reference Oliveira LM, Tuchin VV (2022) Optical clearing for cancer diagnostics and monitoring. In: Handbook of Tissue Optical Clearing. CRC Press, pp 597–606 Oliveira LM, Tuchin VV (2022) Optical clearing for cancer diagnostics and monitoring. In: Handbook of Tissue Optical Clearing. CRC Press, pp 597–606
110.
go back to reference Ortega S, Halicek M, Fabelo H, Callico GM, Fei B (2020) Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review. Biomed Opt Express 11(6):3195–3233PubMedPubMedCentralCrossRef Ortega S, Halicek M, Fabelo H, Callico GM, Fei B (2020) Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review. Biomed Opt Express 11(6):3195–3233PubMedPubMedCentralCrossRef
111.
go back to reference Panchal R, Horton L, Poozesh P, Baqersad J, Nasiriavanaki M (2019) Vibration analysis of healthy skin: toward a noninvasive skin diagnosis methodology. J Biomed Opt 24(1):015001PubMedPubMedCentralCrossRef Panchal R, Horton L, Poozesh P, Baqersad J, Nasiriavanaki M (2019) Vibration analysis of healthy skin: toward a noninvasive skin diagnosis methodology. J Biomed Opt 24(1):015001PubMedPubMedCentralCrossRef
112.
go back to reference Parvin T, Ahmed K, Alatwi AM, Rashed ANZ (2021) Differential optical absorption spectroscopy-based refractive index sensor for cancer cell detection. Opt Rev 28:134–143CrossRef Parvin T, Ahmed K, Alatwi AM, Rashed ANZ (2021) Differential optical absorption spectroscopy-based refractive index sensor for cancer cell detection. Opt Rev 28:134–143CrossRef
113.
go back to reference Patil PM, Kamat DK (2019) Embedded healthcare system based on bioimpedance analysis for identification and classification of skin diseases in indian context. In: U-Healthcare Monitoring Systems. Elsevier, pp 261–288 Patil PM, Kamat DK (2019) Embedded healthcare system based on bioimpedance analysis for identification and classification of skin diseases in indian context. In: U-Healthcare Monitoring Systems. Elsevier, pp 261–288
114.
go back to reference Pölönen I, Rahkonen S, Annala L, Neittaanmäki N (2019) Convolutional neural networks in skin cancer detection using spatial and spectral domain. In: Photonics in Dermatology and Plastic Surgery 2019, vol 10851. SPIE, pp 21–28 Pölönen I, Rahkonen S, Annala L, Neittaanmäki N (2019) Convolutional neural networks in skin cancer detection using spatial and spectral domain. In: Photonics in Dermatology and Plastic Surgery 2019, vol 10851. SPIE, pp 21–28
115.
go back to reference Rees JR, Zens MS, Celaya MO, Riddle BL, Karagas MR, Peacock JL (2015) Survival after squamous cell and basal cell carcinoma of the skin: a retrospective cohort analysis. Int J Cancer 137(4):878–884PubMedPubMedCentralCrossRef Rees JR, Zens MS, Celaya MO, Riddle BL, Karagas MR, Peacock JL (2015) Survival after squamous cell and basal cell carcinoma of the skin: a retrospective cohort analysis. Int J Cancer 137(4):878–884PubMedPubMedCentralCrossRef
116.
go back to reference Rey-Barroso L, Peña-Gutiérrez S, Yáñez C, Burgos-Fernández FJ, Vilaseca M, Royo S (2021) Optical technologies for the improvement of skin cancer diagnosis: a review. Sensors 21(1):252PubMedPubMedCentralCrossRef Rey-Barroso L, Peña-Gutiérrez S, Yáñez C, Burgos-Fernández FJ, Vilaseca M, Royo S (2021) Optical technologies for the improvement of skin cancer diagnosis: a review. Sensors 21(1):252PubMedPubMedCentralCrossRef
118.
go back to reference Rinaldi AO, Morita H, Wawrzyniak P, Dreher A, Grant S, Svedenhag P, Akdis CA (2019) Direct assessment of skin epithelial barrier by electrical impedance spectroscopy. Allergy 74(10):1934–1944PubMedCrossRef Rinaldi AO, Morita H, Wawrzyniak P, Dreher A, Grant S, Svedenhag P, Akdis CA (2019) Direct assessment of skin epithelial barrier by electrical impedance spectroscopy. Allergy 74(10):1934–1944PubMedCrossRef
119.
go back to reference Robinson JK, Joshi KM, Ortiz S, Kundu RV (2013) Melanoma knowledge, perception, and awareness in ethnic minorities in Chicago: recommendations regarding education. Psychooncology 20(3):313–20CrossRef Robinson JK, Joshi KM, Ortiz S, Kundu RV (2013) Melanoma knowledge, perception, and awareness in ethnic minorities in Chicago: recommendations regarding education. Psychooncology 20(3):313–20CrossRef
120.
go back to reference Rogers HW, Weinstock MA, Feldman SR, Coldiron BM (2015) Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the us population, 2012. JAMA Dermatol 151(10):1081–1086PubMedCrossRef Rogers HW, Weinstock MA, Feldman SR, Coldiron BM (2015) Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the us population, 2012. JAMA Dermatol 151(10):1081–1086PubMedCrossRef
121.
go back to reference Ruffano LF, Takwoingi Y, Dinnes J, Chuchu N, Bayliss SE, Davenport C, Matin RN, Godfrey K, O’Sullivan C, Gulati A et al (2018) Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults. Cochrane Database Syst Rev (12) Ruffano LF, Takwoingi Y, Dinnes J, Chuchu N, Bayliss SE, Davenport C, Matin RN, Godfrey K, O’Sullivan C, Gulati A et al (2018) Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults. Cochrane Database Syst Rev (12)
122.
go back to reference Sala A, Anderson DJ, Brennan PM, Butler HJ, Cameron JM, Jenkinson MD, Rinaldi C, Theakstone AG, Baker MJ (2020) Biofluid diagnostics by Ftir spectroscopy: a platform technology for cancer detection. Cancer Lett 477:122–130PubMedCrossRef Sala A, Anderson DJ, Brennan PM, Butler HJ, Cameron JM, Jenkinson MD, Rinaldi C, Theakstone AG, Baker MJ (2020) Biofluid diagnostics by Ftir spectroscopy: a platform technology for cancer detection. Cancer Lett 477:122–130PubMedCrossRef
123.
go back to reference Saladi RN, Persaud AN (2005) The causes of skin cancer: a comprehensive review. Drugs Today 41(1):37–54CrossRef Saladi RN, Persaud AN (2005) The causes of skin cancer: a comprehensive review. Drugs Today 41(1):37–54CrossRef
124.
go back to reference Salim M, Wåhlin E, Dembrower K, Azavedo E, Foukakis T, Liu Y, Smith K, Eklund M, Strand F (2020) External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms. JAMA Oncol 6(10):1581–1588PubMedPubMedCentralCrossRef Salim M, Wåhlin E, Dembrower K, Azavedo E, Foukakis T, Liu Y, Smith K, Eklund M, Strand F (2020) External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms. JAMA Oncol 6(10):1581–1588PubMedPubMedCentralCrossRef
126.
go back to reference Schmid-Saugeona P, Guillodb J, Thirana J-P (2003) Towards a computer-aided diagnosis system for pigmented skin lesions. Comput Med Imaging Graph 27(1):65–78PubMedCrossRef Schmid-Saugeona P, Guillodb J, Thirana J-P (2003) Towards a computer-aided diagnosis system for pigmented skin lesions. Comput Med Imaging Graph 27(1):65–78PubMedCrossRef
128.
go back to reference Shoo BA, Kashani-Sabet M (2000) Melanoma arising in African-, Asian-, Latino- and native-American populations. Semin Cutan Med Surg 28(2):96–102CrossRef Shoo BA, Kashani-Sabet M (2000) Melanoma arising in African-, Asian-, Latino- and native-American populations. Semin Cutan Med Surg 28(2):96–102CrossRef
129.
go back to reference S J, H L, Z J (2021) A visually interpretable deep learning framework for histopathological image-based skin cancer diagnosis vol 25. Institute of Electrical and Electronics Engineers Inc., pp 1483–1494 S J, H L, Z J (2021) A visually interpretable deep learning framework for histopathological image-based skin cancer diagnosis vol 25. Institute of Electrical and Electronics Engineers Inc., pp 1483–1494
130.
go back to reference Simelane NWN, Kruger CA, Abrahamse H (2020) Photodynamic diagnosis and photodynamic therapy of colorectal cancer in vitro and in vivo. RSC Adv 10(68):41560–41576CrossRef Simelane NWN, Kruger CA, Abrahamse H (2020) Photodynamic diagnosis and photodynamic therapy of colorectal cancer in vitro and in vivo. RSC Adv 10(68):41560–41576CrossRef
131.
go back to reference Singh S, Kasana SS (2019) Estimation of soil properties from the Eu spectral library using long short-term memory networks. Geoderma Reg 18:00233 Singh S, Kasana SS (2019) Estimation of soil properties from the Eu spectral library using long short-term memory networks. Geoderma Reg 18:00233
132.
go back to reference Singh S, Kasana SS (2022) Quantitative estimation of soil properties using hybrid features and RNN variants. Chemosphere 287:131889PubMedCrossRef Singh S, Kasana SS (2022) Quantitative estimation of soil properties using hybrid features and RNN variants. Chemosphere 287:131889PubMedCrossRef
133.
go back to reference Singh D, Singh AK (2020) Role of image thermography in early breast cancer detection-past, present and future. Comput Methods Programs Biomed 183:105074PubMedCrossRef Singh D, Singh AK (2020) Role of image thermography in early breast cancer detection-past, present and future. Comput Methods Programs Biomed 183:105074PubMedCrossRef
134.
go back to reference Sinz C, Tschandl P, Rosendahl C, Akay BN, Argenziano G, Blum A, Braun RP, Cabo H, Gourhant J-Y, Kreusch J et al (2017) Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin. J Am Acad Dermatol 77(6):1100–1109PubMedCrossRef Sinz C, Tschandl P, Rosendahl C, Akay BN, Argenziano G, Blum A, Braun RP, Cabo H, Gourhant J-Y, Kreusch J et al (2017) Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin. J Am Acad Dermatol 77(6):1100–1109PubMedCrossRef
135.
go back to reference Sood R, Rositch AF, Shakoor D, Ambinder E, Pool K-L, Pollack E, Mollura DJ, Mullen LA, Harvey SC (2019) Ultrasound for breast cancer detection globally: a systematic review and meta-analysis. J Glob Oncol 5:1–17PubMed Sood R, Rositch AF, Shakoor D, Ambinder E, Pool K-L, Pollack E, Mollura DJ, Mullen LA, Harvey SC (2019) Ultrasound for breast cancer detection globally: a systematic review and meta-analysis. J Glob Oncol 5:1–17PubMed
136.
go back to reference Stang A, Schuldt K, Trocchi P, Neusser S, Speckemeier C, Pahmeier K, Wasem J, Lax H, Nonnemacher M (2022) The impossibility of mortality evaluation of skin cancer screening in Germany based on health insurance data: a case-control study. Eur J Cancer 173:52–58PubMedCrossRef Stang A, Schuldt K, Trocchi P, Neusser S, Speckemeier C, Pahmeier K, Wasem J, Lax H, Nonnemacher M (2022) The impossibility of mortality evaluation of skin cancer screening in Germany based on health insurance data: a case-control study. Eur J Cancer 173:52–58PubMedCrossRef
137.
go back to reference Stern RS (2010) Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch Dermatol 146(3):279–282PubMedCrossRef Stern RS (2010) Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch Dermatol 146(3):279–282PubMedCrossRef
138.
go back to reference Takiddin A, Schneider J, Yang Y, A A-A, Househ M (2018) Artificial intelligence for skin cancer detection: scoping review. J Med Internet Res 23(11):22934 Takiddin A, Schneider J, Yang Y, A A-A, Househ M (2018) Artificial intelligence for skin cancer detection: scoping review. J Med Internet Res 23(11):22934
139.
go back to reference Tamas T, Dinu C, Lenghel M, Băciuţ G, Bran S, Stoia S, Băciuţ M (2021) The role of ultrasonography in head and neck non-melanoma skin cancer approach: an update with a review of the literature. Med Ultrason 23(1):83–88PubMedCrossRef Tamas T, Dinu C, Lenghel M, Băciuţ G, Bran S, Stoia S, Băciuţ M (2021) The role of ultrasonography in head and neck non-melanoma skin cancer approach: an update with a review of the literature. Med Ultrason 23(1):83–88PubMedCrossRef
140.
go back to reference Tan TY, Zhang L, Neoh SC, Lim CP (2018) Intelligent skin cancer detection using enhanced particle swarm optimization. Knowl-Based Syst 158:118–135CrossRef Tan TY, Zhang L, Neoh SC, Lim CP (2018) Intelligent skin cancer detection using enhanced particle swarm optimization. Knowl-Based Syst 158:118–135CrossRef
141.
go back to reference Tokarz D, Cisek R, Joseph A, Golaraei A, Mirsanaye K, Krouglov S, Asa SL, Wilson BC, Barzda V (2019) Characterization of pancreatic cancer tissue using multiphoton excitation fluorescence and polarization-sensitive harmonic generation microscopy. Front Oncol 9:272PubMedPubMedCentralCrossRef Tokarz D, Cisek R, Joseph A, Golaraei A, Mirsanaye K, Krouglov S, Asa SL, Wilson BC, Barzda V (2019) Characterization of pancreatic cancer tissue using multiphoton excitation fluorescence and polarization-sensitive harmonic generation microscopy. Front Oncol 9:272PubMedPubMedCentralCrossRef
142.
go back to reference Tomaszewski M, Michalski P, Osuchowski J (2021) Object description based on local features repeatability. In: Control, Computer Engineering and Neuroscience: Proceedings of IC Brain Computer Interface 2021. Springer, pp 255–267 Tomaszewski M, Michalski P, Osuchowski J (2021) Object description based on local features repeatability. In: Control, Computer Engineering and Neuroscience: Proceedings of IC Brain Computer Interface 2021. Springer, pp 255–267
143.
go back to reference Tomaszewski M, Osuchowski J, Debita Ł (2018) Effect of spatial filtering on object detection with the surf algorithm. In: Biomedical Engineering and Neuroscience: Proceedings of the 3rd International Scientific Conference on Brain-Computer Interfaces, BCI 2018, March 13-14, Opole, Poland. Springer, pp 121–140 Tomaszewski M, Osuchowski J, Debita Ł (2018) Effect of spatial filtering on object detection with the surf algorithm. In: Biomedical Engineering and Neuroscience: Proceedings of the 3rd International Scientific Conference on Brain-Computer Interfaces, BCI 2018, March 13-14, Opole, Poland. Springer, pp 121–140
144.
go back to reference Trager MH, Geskin LJ, Samie FH, Liu L (2022) Biomarkers in melanoma and non-melanoma skin cancer prevention and risk stratification. Exp Dermatol 31(1):4–12PubMedCrossRef Trager MH, Geskin LJ, Samie FH, Liu L (2022) Biomarkers in melanoma and non-melanoma skin cancer prevention and risk stratification. Exp Dermatol 31(1):4–12PubMedCrossRef
146.
go back to reference Urbanos G, Martín A, Vázquez G, Villanueva M, Villa M, Jimenez-Roldan L, Chavarrías M, Lagares A, Juárez E, Sanz C (2021) Supervised machine learning methods and hyperspectral imaging techniques jointly applied for brain cancer classification. Sensors 21(11):3827PubMedPubMedCentralCrossRef Urbanos G, Martín A, Vázquez G, Villanueva M, Villa M, Jimenez-Roldan L, Chavarrías M, Lagares A, Juárez E, Sanz C (2021) Supervised machine learning methods and hyperspectral imaging techniques jointly applied for brain cancer classification. Sensors 21(11):3827PubMedPubMedCentralCrossRef
147.
go back to reference Vardasca R (2020) Comparison of machine learning strategies for infrared thermography of skin cancer Vardasca R (2020) Comparison of machine learning strategies for infrared thermography of skin cancer
148.
go back to reference Verstockt J, Verspeek S, Thiessen F, Tjalma WA, Brochez L, Steenackers G (2022) Skin cancer detection using infrared thermography: measurement setup, procedure and equipment. Sensors 22(9):3327PubMedPubMedCentralCrossRef Verstockt J, Verspeek S, Thiessen F, Tjalma WA, Brochez L, Steenackers G (2022) Skin cancer detection using infrared thermography: measurement setup, procedure and equipment. Sensors 22(9):3327PubMedPubMedCentralCrossRef
149.
go back to reference Villani A, Potestio L, Fabbrocini G, Troncone G, Malapelle U, Scalvenzi M (2022) The treatment of advanced melanoma: therapeutic updates. Int J Mol Sci 23(12):6388PubMedPubMedCentralCrossRef Villani A, Potestio L, Fabbrocini G, Troncone G, Malapelle U, Scalvenzi M (2022) The treatment of advanced melanoma: therapeutic updates. Int J Mol Sci 23(12):6388PubMedPubMedCentralCrossRef
150.
go back to reference Wang Y, Wang N, Xu M, Yu J, Qin C, Luo X, Yang X, Wang T, Li A, Ni D (2019) Deeply-supervised networks with threshold loss for cancer detection in automated breast ultrasound. IEEE Trans Med Imaging 39(4):866–876PubMedCrossRef Wang Y, Wang N, Xu M, Yu J, Qin C, Luo X, Yang X, Wang T, Li A, Ni D (2019) Deeply-supervised networks with threshold loss for cancer detection in automated breast ultrasound. IEEE Trans Med Imaging 39(4):866–876PubMedCrossRef
151.
153.
go back to reference Young AT, Vora NB, Cortez J, Tam A, Yeniay Y, Afifi L, Yan D, Nosrati A, Wong A, Johal A et al (2021) The role of technology in melanoma screening and diagnosis. Pigment Cell Melanoma Res 34(2):288–300PubMedCrossRef Young AT, Vora NB, Cortez J, Tam A, Yeniay Y, Afifi L, Yan D, Nosrati A, Wong A, Johal A et al (2021) The role of technology in melanoma screening and diagnosis. Pigment Cell Melanoma Res 34(2):288–300PubMedCrossRef
154.
go back to reference Zambrano-Román M, Padilla-Gutiérrez JR, Valle Y, Muñoz-Valle JF, Valdés-Alvarado E (2022) Non-melanoma skin cancer: a genetic update and future perspectives. Cancers 14(10):2371PubMedPubMedCentralCrossRef Zambrano-Román M, Padilla-Gutiérrez JR, Valle Y, Muñoz-Valle JF, Valdés-Alvarado E (2022) Non-melanoma skin cancer: a genetic update and future perspectives. Cancers 14(10):2371PubMedPubMedCentralCrossRef
155.
go back to reference Zareen S, Guangmin S, Li Y, Kundi M, Qadri S, Qadri S, Ahmad M, Khan A (2022) A machine vision approach for classification of skin cancer using hybrid texture features. Comput Intell Neurosci 2022:4942637PubMedPubMedCentralCrossRef Zareen S, Guangmin S, Li Y, Kundi M, Qadri S, Qadri S, Ahmad M, Khan A (2022) A machine vision approach for classification of skin cancer using hybrid texture features. Comput Intell Neurosci 2022:4942637PubMedPubMedCentralCrossRef
156.
go back to reference Zeebaree DQ, Abdulazeez AM, Zebari DA, Haron H, Hamed HNA (2021) Multi-level fusion in ultrasound for cancer detection based on uniform lbp features. Comput Mater Continua 66(3):3363–3382CrossRef Zeebaree DQ, Abdulazeez AM, Zebari DA, Haron H, Hamed HNA (2021) Multi-level fusion in ultrasound for cancer detection based on uniform lbp features. Comput Mater Continua 66(3):3363–3382CrossRef
157.
go back to reference Zhou Z, Liu J, Huang J, Rees TW, Wang Y, Wang H, Li X, Chao H, Stang PJ (2019) A self-assembled ru-pt metallacage as a lysosome-targeting photosensitizer for 2-photon photodynamic therapy. Proc Natl Acad Sci 116(41):20296–20302PubMedPubMedCentralCrossRef Zhou Z, Liu J, Huang J, Rees TW, Wang Y, Wang H, Li X, Chao H, Stang PJ (2019) A self-assembled ru-pt metallacage as a lysosome-targeting photosensitizer for 2-photon photodynamic therapy. Proc Natl Acad Sci 116(41):20296–20302PubMedPubMedCentralCrossRef
158.
go back to reference Zhou Y, Shi Y, Lu W, F W (2022) Did artificial intelligence invade humans? The study on the mechanism of patients’ willingness to accept artificial intelligence medical care: from the perspective of intergroup threat theory. Front Psychol 13:866124 Zhou Y, Shi Y, Lu W, F W (2022) Did artificial intelligence invade humans? The study on the mechanism of patients’ willingness to accept artificial intelligence medical care: from the perspective of intergroup threat theory. Front Psychol 13:866124
Metadata
Title
Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes
Authors
Maria Myslicka
Aleksandra Kawala-Sterniuk
Anna Bryniarska
Adam Sudol
Michal Podpora
Rafal Gasz
Radek Martinek
Radana Kahankova Vilimkova
Dominik Vilimek
Mariusz Pelc
Dariusz Mikolajewski
Publication date
01-05-2024
Publisher
Springer Berlin Heidelberg
Published in
Archives of Dermatological Research / Issue 4/2024
Print ISSN: 0340-3696
Electronic ISSN: 1432-069X
DOI
https://doi.org/10.1007/s00403-024-02828-1

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