Skip to main content
Top
Published in: Journal of Medical Systems 1/2019

01-01-2019 | Patient Facing Systems

Artificial Intelligence Based Skin Classification Using GMM

Authors: M. Monisha, A. Suresh, M. R. Rashmi

Published in: Journal of Medical Systems | Issue 1/2019

Login to get access

Abstract

This study describes the usage of neural community based on the texture evaluation of pores and skin a variety of similarities in their signs, inclusive of Measles (rubella), German measles (rubella), and Chickenpox etc. In fashionable, these illnesses have similarities in sample of infection and symptoms along with redness and rash. Various skin problems have similar symptoms. For example, in German measles (rubella), Chicken pox and Measles (rubella) a similarity can be observed in skin rashes and redness. The prognosis of skin problems take a long time as the patient’s previous medical records, physical examination report and the respective laboratory diagnostic reports have to be studied. The recognition and diagnosis get tough due to the complexity involved. Subsequently, a computer aided analysis and recognition gadget would be handy in such cases. Computer algorithm steps include image processing, picture characteristic extraction and categorize facts with the help of a classifier with Artificial Neural Network (ANN). The ANN can analyze the patterns of symptoms of a particular disease and present faster prognosis and reputation than a human doctor. For this reason, the patients can undergo the treatment for the pores and skin problems based totally on the symptoms detected.
Literature
1.
go back to reference Bono, A., Tomatis, S., and Bartoli, C., The ABCD machine of melanoma detection: A spectrophotometric evaluation of the asymmetry, border, shade, and measurement. Most Cancers 85(1):72–77, 1999. Bono, A., Tomatis, S., and Bartoli, C., The ABCD machine of melanoma detection: A spectrophotometric evaluation of the asymmetry, border, shade, and measurement. Most Cancers 85(1):72–77, 1999.
2.
go back to reference Pehamberger, H., Binder, M., Steiner, A., and Wolff, K., In vivo epiluminescence microscopy: development of early diagnosis of melanoma. J Make Investments Dermatol 100:356S–362S, 1993.CrossRef Pehamberger, H., Binder, M., Steiner, A., and Wolff, K., In vivo epiluminescence microscopy: development of early diagnosis of melanoma. J Make Investments Dermatol 100:356S–362S, 1993.CrossRef
3.
go back to reference Bafounta, M. L., Beauchet, A., and Aegerter, P., Saiag P. Is dermoscopy (epiluminescence microscopy) beneficial for the prognosis of cancer? Results of a meta-evaluation the usage of strategies adapted to the evaluation of diagnostic checks. Arch. Dermatol. 137(13):43–50, 2001. Bafounta, M. L., Beauchet, A., and Aegerter, P., Saiag P. Is dermoscopy (epiluminescence microscopy) beneficial for the prognosis of cancer? Results of a meta-evaluation the usage of strategies adapted to the evaluation of diagnostic checks. Arch. Dermatol. 137(13):43–50, 2001.
4.
go back to reference Argenziano, G., Soyer, H., Chimenti, S., Talamini, R., Corona, R., Sera, F., and Binder, M., Dermoscopy of pigmented pores and skin lesions: effects of consensus assembly via the net magazine of the yank. Academy of Dermatology 48:679–693, 2003.CrossRef Argenziano, G., Soyer, H., Chimenti, S., Talamini, R., Corona, R., Sera, F., and Binder, M., Dermoscopy of pigmented pores and skin lesions: effects of consensus assembly via the net magazine of the yank. Academy of Dermatology 48:679–693, 2003.CrossRef
5.
go back to reference Garnavi, R., Computer-aided prognosis of melanoma, Ph.D. dissertation. Australia: College of Melbourne, 2011. Garnavi, R., Computer-aided prognosis of melanoma, Ph.D. dissertation. Australia: College of Melbourne, 2011.
6.
go back to reference Celebi, M. E., Iyatomi, H., Schaefer, G., and Stoecker, W. V., Lesion border detection in dermoscopy images. Computerised Scientific Imaging and Portraits 33(2):148–153, 2009. Celebi, M. E., Iyatomi, H., Schaefer, G., and Stoecker, W. V., Lesion border detection in dermoscopy images. Computerised Scientific Imaging and Portraits 33(2):148–153, 2009.
7.
go back to reference Iyatomi, H., Oka, H., Saito, M., Miyake, A., Kimoto, M., Yamagami, J., Kobayashi, S., Tanikawa, A., Hagiwara, M., Ogawa, K., Argenziano, G., Soyer, H. P., and Tanaka, M., Quantitative assessment of tumour extraction from dermoscopy photos and assessment of pc-primarily based extraction strategies for an automatic cancer diagnostic gadget. Melanoma Studies 16(2):183–190, 2006.CrossRef Iyatomi, H., Oka, H., Saito, M., Miyake, A., Kimoto, M., Yamagami, J., Kobayashi, S., Tanikawa, A., Hagiwara, M., Ogawa, K., Argenziano, G., Soyer, H. P., and Tanaka, M., Quantitative assessment of tumour extraction from dermoscopy photos and assessment of pc-primarily based extraction strategies for an automatic cancer diagnostic gadget. Melanoma Studies 16(2):183–190, 2006.CrossRef
8.
go back to reference Ng, V., Fung, B., and Lee, T., Determining the asymmetry of skin lesion with fuzzy borders. Comput. Biol. Med. 35:103–120, 2005.CrossRef Ng, V., Fung, B., and Lee, T., Determining the asymmetry of skin lesion with fuzzy borders. Comput. Biol. Med. 35:103–120, 2005.CrossRef
9.
go back to reference Pehamberger, H., Steiner, A., and Wolff, O. K., In vivo epiluminescence microscopy of pigmented skin lesions. i. Pattern evaluation of pigmented pores and skin lesions. J. Am. Acad. Dermatol. 17(4):571–583, 1987.CrossRef Pehamberger, H., Steiner, A., and Wolff, O. K., In vivo epiluminescence microscopy of pigmented skin lesions. i. Pattern evaluation of pigmented pores and skin lesions. J. Am. Acad. Dermatol. 17(4):571–583, 1987.CrossRef
10.
go back to reference Garnavi, R., Aldeen, M., and Bailey, J., Laptop-aided diagnosis of melanoma using border-and wavelet-based texture evaluation. IEEE Trans. Inf. Technol. Biomed. 16(6):1239–1252, 2012.CrossRef Garnavi, R., Aldeen, M., and Bailey, J., Laptop-aided diagnosis of melanoma using border-and wavelet-based texture evaluation. IEEE Trans. Inf. Technol. Biomed. 16(6):1239–1252, 2012.CrossRef
11.
go back to reference Patwardhan, S. V., Dhawan, A. P., and Relue, P. A., Type of cancer the usage of tree based wavelet transforms. Comput. Methods Prog. Biomed. 22(3):223–239, 2003.CrossRef Patwardhan, S. V., Dhawan, A. P., and Relue, P. A., Type of cancer the usage of tree based wavelet transforms. Comput. Methods Prog. Biomed. 22(3):223–239, 2003.CrossRef
12.
go back to reference Ramezani, M., Karimian, A., and Moallem, P., Automatic detection of malignant cancer using macroscopic snap shots. J. Med. alerts Sens. 4(4):281, 2014. Ramezani, M., Karimian, A., and Moallem, P., Automatic detection of malignant cancer using macroscopic snap shots. J. Med. alerts Sens. 4(4):281, 2014.
13.
go back to reference Di Leo, G., Paolillo, A., Sommella, P., et al., Automatic analysis of cancer: a software machine primarily based at the 7-point test-list. 2010 forty third Hawaii Int. Conf. on gadget Sciences (HICSS), 2010. Di Leo, G., Paolillo, A., Sommella, P., et al., Automatic analysis of cancer: a software machine primarily based at the 7-point test-list. 2010 forty third Hawaii Int. Conf. on gadget Sciences (HICSS), 2010.
14.
go back to reference Burroni, M., Corona, R., Dell’Eva, G. et al., Melanoma computer-aided analysis reliability and feasibility observe. Clin. Cancer Res. 10(6):1881–1886, 2004.CrossRef Burroni, M., Corona, R., Dell’Eva, G. et al., Melanoma computer-aided analysis reliability and feasibility observe. Clin. Cancer Res. 10(6):1881–1886, 2004.CrossRef
15.
go back to reference Piccolo, D., Crisman, G., Schoinas, S. et al., Laptop-automated ABCD versus dermatologists with different levels of experience in dermoscopy. Eur. J. Dermatol. 24(4):477–481, 2014. Piccolo, D., Crisman, G., Schoinas, S. et al., Laptop-automated ABCD versus dermatologists with different levels of experience in dermoscopy. Eur. J. Dermatol. 24(4):477–481, 2014.
16.
go back to reference Ramteke, N. S., and Jain, S. V., BCD rule based automatic computer-aided skin most cancers detection the use of Matlab®. Int. J. Comput. Technol. Appl. 4(4):691, 2013. Ramteke, N. S., and Jain, S. V., BCD rule based automatic computer-aided skin most cancers detection the use of Matlab®. Int. J. Comput. Technol. Appl. 4(4):691, 2013.
17.
go back to reference Smaoui, N., and Bessassi, S., A advanced device for cancer analysis. Int. J. Comput. Vis. Sign Manner. 3(1):10–17, 2013. Smaoui, N., and Bessassi, S., A advanced device for cancer analysis. Int. J. Comput. Vis. Sign Manner. 3(1):10–17, 2013.
18.
go back to reference Celebi, M. E., Iyatomi, H., and Stoecker, W. V., Automatic detection of blue-white veil and related structures in dermoscopy photos. Comput. Med. Imaging Graph. 32(8):670–677, 2008.CrossRef Celebi, M. E., Iyatomi, H., and Stoecker, W. V., Automatic detection of blue-white veil and related structures in dermoscopy photos. Comput. Med. Imaging Graph. 32(8):670–677, 2008.CrossRef
19.
go back to reference Ferris, L. O. K., Harkes, J. A., Gilbert, B. et al., Laptop-aided classification of melanocytic lesions the use of dermoscopic photos. J. Am. Acad. Dermatol. 73(5):769–776, 2015.CrossRef Ferris, L. O. K., Harkes, J. A., Gilbert, B. et al., Laptop-aided classification of melanocytic lesions the use of dermoscopic photos. J. Am. Acad. Dermatol. 73(5):769–776, 2015.CrossRef
20.
go back to reference Celebi, M., Kingravi, H., Uddin, B., Iyatomi, H., Aslandogan, Y., Stoecker, W., and Moss, R., A methodological approach to the classification of dermoscopy pics. Automated Medical Imaging and Photographs 31:362–373, 2007. Celebi, M., Kingravi, H., Uddin, B., Iyatomi, H., Aslandogan, Y., Stoecker, W., and Moss, R., A methodological approach to the classification of dermoscopy pics. Automated Medical Imaging and Photographs 31:362–373, 2007.
21.
go back to reference Garnavi, R., Aldeen, M., Celebi, M. E., Bhuiyan, A., Dolianitis, C., and Varigos, G., Automatic segmentation of dermoscopy pics using histogram thresholding on finest colour channels. Global Journal of Medicine and Medical Sciences 1(2):126–134, 2010. Garnavi, R., Aldeen, M., Celebi, M. E., Bhuiyan, A., Dolianitis, C., and Varigos, G., Automatic segmentation of dermoscopy pics using histogram thresholding on finest colour channels. Global Journal of Medicine and Medical Sciences 1(2):126–134, 2010.
Metadata
Title
Artificial Intelligence Based Skin Classification Using GMM
Authors
M. Monisha
A. Suresh
M. R. Rashmi
Publication date
01-01-2019
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 1/2019
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-018-1112-5

Other articles of this Issue 1/2019

Journal of Medical Systems 1/2019 Go to the issue