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

• URL: http://arxiv.org/abs/2201.09952v1
• Date: Mon, 24 Jan 2022 21:12:25 GMT
• Title: A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks
• Authors: Aditya Saxena and Shamsheer Pal Singh
• Abstract summary: COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China. It has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China and by now has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths. With limited number of COVID-19 test kits available in medical facilities, it is important to develop and implement an automatic detection system as an alternative diagnosis option for COVID-19 detection that can used on a commercial scale. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Computer vision and deep learning techniques can help in determining COVID-19 virus with Chest X-ray Images. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural network for image analysis and classification. In this research, we have proposed a deep convolutional neural network trained on five open access datasets with binary output: Normal and Covid. The performance of the model is compared with four pre-trained convolutional neural network-based models (COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that the proposed model provides better accuracy on the validation set as compared to the other four pre-trained models. This research work provides promising results which can be further improvise and implement on a commercial scale.

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