Top

Published in:

01-06-2021 | Computed Tomography | Original Article

Diagnosis of COVID-19 using CT scan images and deep learning techniques

Authors: Vruddhi Shah, Rinkal Keniya, Akanksha Shridharani, Manav Punjabi, Jainam Shah, Ninad Mehendale

Published in: Emergency Radiology | Issue 3/2021

Abstract

Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method is based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 19 CT using different deep learning techniques. A self-developed model named CTnet-10 was designed for the COVID-19 diagnosis, having an accuracy of 82.1%. Also, other models that we tested are DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19. The VGG-19 proved to be superior with an accuracy of 94.52% as compared to all other deep learning models. Automated diagnosis of COVID-19 from the CT scan pictures can be used by the doctors as a quick and efficient method for COVID-19 screening.

Available only for authorised users

Wang C, Horby PW, Hayden FG, Gao GF (2020) A novel coronavirus outbreak of global health concern. Lancet 395(10223):470CrossRef

Wang LS, Wang YR, Ye DW, Liu QQ (2020) A review of the 2019 novel coronavirus (covid-19) based on current evidence. International journal of antimicrobial agents, 105948

Singhal T (2020) A review of coronavirus disease-2019 (covid-19). The Indian Journal of Pediatrics, 1–6

Wang R, Pan M, Zhang X, Han M, Fan X, Zhao F, Miao M, Xu J, Guan M, Deng X et al (2020) Epidemiological and clinical features of 125 hospitalized patients with covid-19 in Fuyang, Anhui, China. Int J Infect Dis 95:421CrossRef

Tang YW, Schmitz JE, Persing DH, Stratton CW (2020) Laboratory diagnosis of covid-19: current issues and challenges. J Clin Microbiol 58(6):512–520. https://doi.org/10.1128/JCM.00512-20CrossRef

Hussain A, Kaler J, Tabrez E, Tabrez S, Tabrez SS (2020) Novel covid-19: a comprehensive review of transmission, manifestation, and pathogenesis. Cureus 12(5):8184. https://doi.org/10.7759/cureus.8184

Li B, Yang J, Zhao F, Zhi L, Wang X, Liu L, Bi Z, Zhao Y (2020) Prevalence and impact of cardiovascular metabolic diseases on covid-19 in China. Clin Res Cardiol 109(5):531CrossRef

Liu Z, Xiao X, Wei X, Li J, Yang J, Tan H, Zhu J, Zhang Q, Wu J, Liu L (2020) Composition and divergence of coronavirus spike proteins and host ace2 receptors predict potential intermediate hosts of sars-cov-2. J Med Virol 92(6):595CrossRef

Breban R, Riou J, Fontanet A (2013) Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk. Lancet 382(9893):694CrossRef

10.

Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of covid-19 cases using deep neural networks with x-ray images. Computers in Biology and Medicine, 103792

11.

Singh D, Kumar V, Kaur M (2020) Classification of covid-19 patients from chest ct images using multi-objective differential evolution–based convolutional neural networks. European Journal of Clinical Microbiology & Infectious Diseases, 1–11

12.

Anjishnu Das SK (2020) Why covid testing is a slow process and types of tests available

13.

Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Ann Rev Biomed Eng 19:221CrossRef

14.

Ker J, Wang L, Rao J, Lim T (2017) Deep learning applications in medical image analysis. Ieee Access 6:9375CrossRef

15.

Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) Radnet: Radiologist level accuracy using deep learning for hemorrhage detection in ct scans. In: IEEE 15th International symposium on biomedical imaging (ISBI 2018). IEEE, pp 281–284

16.

Song Q, Zhao L, Luo X, Dou X (2017) Using deep learning for classification of lung nodules on computed tomography images. Journal of healthcare engineering, 2017

17.

González G, Ash SY, Vegas-Sánchez-Ferrero G, Onieva Onieva J, Rahaghi FN, Ross JC, Díaz A, San José Estépar R, Washko GR (2018) Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am J Respir Crit Care Med 197(2):193CrossRef

18.

Ye Z, Zhang Y, Wang Y, Huang Z, Song B (2020) Chest ct manifestations of new coronavirus disease 2019 (covid-19): a pictorial review. European Radiology, 1–9

19.

Zhao W, Zhong Z, Xie X, Yu Q, Liu J (2020) Relation between chest CT findings and clinical conditions of coronavirus disease (covid-19) pneumonia: a multicenter study. Am J Roentgenol 214 (5):1072CrossRef

20.

Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, Diao K, Lin B, Zhu X, Li K et al (2020) Chest CT findings in coronavirus disease-19 (covid-19): relationship to duration of infection. Radiology, 200463

21.

Zhao J, Zhang Y, He X, Xie P (2020) Covid-CT-dataset: a CT scan dataset about covid-19. arXiv:2003.13865

22.

Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E (2020) Rapid ai development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv:2003.05037

23.

Wu J, Wu X, Zeng W, Guo D, Fang Z, Chen L, Huang H, Li C (2020) Chest CT findings in patients with coronavirus disease 2019 and its relationship with clinical features. Investig Radiol 55 (5):257CrossRef

24.

Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Liu W, Wang X (2020) Deep learning-based detection for covid-19 from chest CT using weak label, medRxiv

25.

Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L (2020) Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (covid-19) in China: a report of 1014 cases. Radiology, 200642

26.

Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W (2020) Sensitivity of chest CT for covid-19: comparison to RT-PCR. Radiology, 200432

27.

Murphy K, Smits H, Knoops AJ, Korst MB, Samson T, Scholten ET, Schalekamp S, Schaefer-Prokop CM, Philipsen RH, Meijers A et al (2020) Covid-19 on the chest radiograph: a multi-reader evaluation of an AI system. Radiology, 201874

28.

Lee CI, Forman HP (2007) Ct screening for lung cancer: implications on social responsibility. Am J Roentgenol 188(2): 297CrossRef

Title: Diagnosis of COVID-19 using CT scan images and deep learning techniques
Authors: Vruddhi Shah
Rinkal Keniya
Akanksha Shridharani
Manav Punjabi
Jainam Shah
Ninad Mehendale
Publication date: 01-06-2021
Publisher: Springer International Publishing
Keywords: Computed Tomography
Computed Tomography
COVID-19
Published in: Emergency Radiology / Issue 3/2021
Print ISSN: 1070-3004
Electronic ISSN: 1438-1435
DOI: https://doi.org/10.1007/s10140-020-01886-y

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Diagnosis of COVID-19 using CT scan images and deep learning techniques

Abstract

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 3/2021

A virtual emergency: learning lessons from remote medical student education during the COVID-19 pandemic

Renal vein injuries: a 10-year, single institution experience with a rare injury following blunt abdominal trauma

Post-traumatic spinal hematoma in ankylosing spondylitis

Short-term experience with aspiration embolectomy using a ACE68 reperfusion catheter to treat embolic superior mesenteric artery occlusion

Spontaneous bilateral orbital subperiosteal hemorrhage resulting from thoracoabdominal crush injury

Automated processing of social media content for radiologists: applied deep learning to radiological content on twitter during COVID-19 pandemic