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The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acuterespiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identifiedin mid-December 2019 in the Hubei province of Wuhan, China and by now hasspread throughout the planet with more than 75.5 million confirmed cases andmore than 1.67 million deaths. With limited number of COVID-19 test kitsavailable in medical facilities, it is important to develop and implement anautomatic detection system as an alternative diagnosis option for COVID-19detection that can used on a commercial scale. Chest X-ray is the first imagingtechnique that plays an important role in the diagnosis of COVID-19 disease.Computer vision and deep learning techniques can help in determining COVID-19virus with Chest X-ray Images. Due to the high availability of large-scaleannotated image datasets, great success has been achieved using convolutionalneural network for image analysis and classification. In this research, we haveproposed a deep convolutional neural network trained on five open accessdatasets with binary output: Normal and Covid. The performance of the model iscompared with four pre-trained convolutional neural network-based models(COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that theproposed model provides better accuracy on the validation set as compared tothe other four pre-trained models. This research work provides promisingresults which can be further improvise and implement on a commercial scale.

A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks