COVID-19 Detection in CT Images using Deep Transfer Learning
Keywords:VGG-16, Deep learning, CT images, Convolution neural networks (CNN)
Confronting the COVID-19 pandemic introduced by newest corona virus, SARS-CoV-2, is one of the human species' most influential problems today. The fast identification and isolation of infected patients is a crucial factor in slowing down the spread of the virus. The Reverse Transcription Polymerase Chain Reaction (RT-PCR) process, one of the basic methods for COVID-19 recognition, is time-consuming in addition short-lived due to the pandemic. Deep learning applied to patients' CT images has given away hopeful results popular the identification of COVID-19 in this context. The powerful net family of CNN models using CT images to perform COVID-19 recognition is suggested in this article by VGG-16. As a consequence, COVID-19 detection was proposed as a VGG-16 model with an overall accuracy of 98.33 percent. We assume that, both in terms of productivity and efficiency, the published figures reflect modern outcomes.
M. A. Shereen, S. Khan, A. Kazmi, N. Bashir, and R. Siddique, “COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses,” Journal of advanced research, Vol. 24, pp. 91–98, 2020.
G Zhang, S. Jiang, Z. Yang, L. Gong, X. Ma, Z. Zhou, C. Baio, Q. Liu “Automatic nodule detection for lung cancer in CT images: a review,” Computers in biology and medicine, Vol. 103, pp. 287–300, 2018.
H. Farhat, G. E. Sakr, and R. Kilany “Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19", Machine vision and applications, Vol. 31, No. 6, pp. 1-42, Sep -2020.
S. H. Kassani, P. H. Kassasni, M. J. Wesolowski, K. A. Schneider, and R. Deters, “Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach", arXiv preprint arXiv, Apr. 2020
P. F. Shan, “Lung Infection Quantification of COVID-19 in CT Images with Deep Learning Author,” arXiv preprint arXiv, 2020.
H. S. Maghdid, A. T. Asaad, K. Z. Ghafoor, A. S. Sadiq, and M. K. Khan, “Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms,” arXiv preprint arXiv, pp. 1–8, 2020.
M. E. H. Chowdhury et al., “Can AI help in screening Viral and COVID-19 pneumonia?,” arXiv preprint arXiv, 2020.
Y. Peng, Y.-X. Tang, S. Lee, Y. Zhu, R. M. Summers, and Z. Lu, “COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature,” arXiv preprint arXiv, Vol. 2, pp. 1–20, 2020.
S. Ying “Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images,” medRxiv, pp. 1–10, 2020, doi: 10.1101/2020.02.23.20026930.
Misbah, Shahzadi, Abrar Ahmad, Muhammad Hammad Butt, Yusra Habib Khan, Nasser Hadal Alotaibi, and Tauqeer Hussain Mallhi. "A systematic analysis of studies on corona virus disease 19 (COVID-19) from viral emergence to treatment." Journal of the College of Physicians and Surgeons, Vol. 30, No. 6 pp. 9-18, 2020.
T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. R. Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Computers in biology and medicine, Vol. 121, pp. 103792, 2020.
G. P. Domingues “Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET,” Artificial intelligence reviews, Vol. 53, pp. 4093–4160, 2019.
N. Zhu, D. Zhang, W. Wang, X. Li, B. Yang, J. Song, X. Zhao "A novel coronavirus from patients with pneumonia in China, 2019." New England Journal of Medicine, 2020.
J. H. Beigel, K. M. Tomashek, L. E. Dodd, A. K. Mehta, B. S. Zingman, A. C. Kalil, E. Hohmann "Remdesivir for the treatment of Covid-19—preliminary report." The New England journal of medicine, 2020.
T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. R. Acharya. "Automated detection of COVID-19 cases using deep neural networks with X-ray images." Computers in Biology and Medicine, Vol. 121, pp. 103792, 2020.
M. F. César, W. E. Dowling, S. G. Funnell, P. Gsell, A. Balta, R. A. Albrecht, H. Andersen "Animal models for COVID-19." Nature, Vol. 586, No. 7830, pp: 509-515, 2020.
S. Liang, M. Xiang, Y. Huafeng, Z. Zhang, P. Bian, Y. Han, J. Sun et al. "The different clinical characteristics of corona virus disease cases between children and their families in China–the character of children with COVID-19", Emerging microbes & infection, Vol. 9, No. 1, pp. 707-713, 2020.
N. Zhu, D. Zhang, W. Wang, L. Xingwang, B. Yang, J. Song, X. hao "A novel coronavirus from patients with pneumonia in China, 2019." New England Journal of Medicine, 2020.
F. Ucar, and D. Korkmaz. "COVID diagnosis-Net: Deep Bayes-SqueezeNet based Diagnostic of the Coronavirus Disease 2019 (COVID-19) from X-Ray Images." Medical Hypotheses, Vol. 140, pp. 109761, 2020.
P. Afshar, S. Heidarian, N. Enshaei, F. Naderkhani, M. J. Rafiee, A. Oikonomou, F. B. Fard, K. Samimi, K. N. Plataniotis, and A. Mohammadi. "COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable in Machine Learning and Deep Learning." arXiv preprint arXiv:2009.14623, 2020.
A. Oguzhan, A. K. Sethi, B. W. Patterson, M. Churpek, N. Safdar. "Effect of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the United States: A Simulation Modeling Approach." Annals of internal medicine, 2020.
Y. Simonyan, and N. C. Smith. "Coronavirus Ethics: Judgments of Market Ethics in a Pandemic", Elsevier SSRN, 2020,
How to Cite
Copyright (c) 2020 International Transactions on Electrical Engineering and Computer Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.