CNN Deep-Learning Technique to Detect Covid-19 Using Chest X-ray


Hemalatha Gunasekaran,Rex Macedo Arokiaraj,K. Ramalakshmi,



Covid-19,Chest X-ray image,CNN,VGG16,Transfer learning,


Most of the countries around the world are under locked down due the pandemic. Every country has imposed a strict travel restrictions and has stopped all types of visas and tourist activities. This created a major impact on aviation sector and the tourist sector. Even the people not effected from Covid-19 and in real emergence are not able travel from one place to another. Some countries have laid down quarantine rules, which will be a major hindrance to emergency travelers and for tourists. All passengers traveling are tested for COVID-19 using RT-PCR, which can take between 48 to 72 hours to produce the result.  But in some cases people who are tested negative even after 3 or 4 RT-PCR tests shows a typical pneumonia in the CT Scan or in a chest X-ray. If the aviation sector relies only on the RT-PCR test, many patients may be missed. In order to reduce the risk to some extent and prevent a high-risk patient from traveling, the passenger can be asked to upload his / her chest X-ray prior to travel. Using an X-ray of the chest, we can predict the possibility of Covid-19 cases before the patients are physically examined. This technique cannot replace the RT-PCR test, but can be a stand-by tool to help detect Covid-19 prior to the RT-PCR test. It would also help to identify patients who are highly prone for the infection. In this paper, we developed a CNN from scratch to identify a patient infected with COVID from a chest X-ray image. The model was trained with the chest X-ray of normal and COVID patients. Later the model was tested on two datasets, one publicly available in GitHub, and the other dataset was compiled from the Italian Society of Medical and Interventional Radiology website using web scrapping. The model produced an accuracy of 96.48 percent with the training dataset. To further improve accuracy, we used the same dataset on a pre-trained network (VGG16) and achieved an accuracy of around 99 per cent.


I. Ali Narin, Ceren Kaya, and Ziynet Pamuk, “Automatic detection of coronavirus disease (COVID-19) using x-ray images and deep convolutional neural networks”,arXiv preprint arXiv:2003.10849, 2020.
II. Asif Iqbal Khan, JunaidLatief Shah, and Mudasir Bhat “Coronet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images”,arXiv preprint arXiv:2004.04931, 2020.
III. Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, et al,“Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest ct. Radiology”,RSNA ,page 200-205, 2020.
IV. LindaWang and AlexanderWong “COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images” arXiv preprint arXiv:2003.09871, 2020.
V. Min Zhou, Yong Chen, Dexiang Wang, Yanping Xu, Weiwu Yao, Jingwen Huang, XiaoyanJin, Zilai Pan, Jingwen Tan, LanWang, et al,“Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia”, medRxiv, 30 March 2020. DOI: 10.1101/2020.03.24.20043117
VI. Ophir Gozes, MaayanFrid-Adar, Hayit Greenspan, Patrick D. Browning, Huangqi Zhang, Wenbin Ji, Adam Bernheim, and Eliot Siegel, “Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis”,arXiv preprint arXiv:2003.05037, 2020.
VII. Rezaul Karim, Till DAűhmen, Dietrich Rebholz-Schuhmann, Stefan Decker, Michael Cochez, Oya
VIII. Beyan, “DeepCOVIDExplainer: Explainable COVID-19 Predictions Based on Chest X-ray Images”,eess.IV, April 2020.
IX. Tulin Ozturk, Muhammed Talo, Eylul Azra Yildirim, Ulas Baran Baloglu, Ozal Yildirim, and U Rajendra Acharya, “Automated detection of covid-19 cases using deep neural networks with x-ray images”, Computers in Biology and Medicine 121, pp. 103792, 2020.
X. World Health Organization. Coronavirus disease 2019 (COVID-19) situation report, June 2020.
XI. Wei-jie Guan, Zheng-yi Ni, Yu Hu,Wen-hua Liang, Chun-quanOu, Jian-xing He, Lei Liu, Hong Shan, Chun-liang Lei, David SC Hui, et al , “Clinical characteristics of coronavirus disease 2019 in china”, The New England Journal of Medicine, med 2020;382, pp.1708-20, 2020

Vasanthselvakumar R, Balasubramanian M, Palanivel S, Detection and Classification of Kidney Disorders using Deep Learning Method”, J.Mech.Cont.& Math.Sci.Vol.-14, No.2, March-April (2019), pp 258-270.

SozanSulaimanMaghdid,Tarik Ahmed Rashid, Sheeraz Ahmed, Khalid Zaman, M.Khalid Rabbani, “Analysis and Prediction of Heart Attacks Based on Design of Intelligent Systems”, J.Mech.Cont.& Math.Sci.Vol.-14, No.-4, July-August (2019), pp 628-645

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