Abstract
Coronavirus disease, also referred to as COVID-19, is a contagious illness generated by a respiratory virus. There has been an exponential increase with the amount of patients affected with COVID-19 that has put an exceptional burden on the medical care frameworks across the world. Analysis of COVID-19 disease from the images of Chest X-ray may help isolate high-risk patients, while test results are anticipated upon. With most X-ray frameworks currently digitized, there is no transportation time required for the samples, hence making it easier for the health care workers to analyze it. In this work, we demonstrate the potential of ResNet, which is a CNN, to diagnose Chest X-ray images. These images can be classified into Normal, COVID, or Viral Pneumonia efficiently using ResNet. As a result, the probability of detecting patients with COVID-19 is maximized through higher accuracy. Empirical analysis exhibits that the proposed neural network strategy is better than Support Vector Machine, Naive Bayes algorithm, Logistic Regression, and k-NN.
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Acknowledgements
The authors are extremely thankful to the faculties of the Department of CSE, Amrita Vishwa Vidyapeetham, Amritapuri Campus for their immense support and fruitful assistance in the fulfillment of this work.
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Hari Prakash, S., Adithya Narayan, K.A., Nair, G.S., Harikumar, S. (2022). Perceiving Machine Learning Algorithms to Analyze COVID-19 Radiographs. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P., Goel, L. (eds) Proceedings of International Conference on Recent Trends in Computing . Lecture Notes in Networks and Systems, vol 341. Springer, Singapore. https://doi.org/10.1007/978-981-16-7118-0_25
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