Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images

Yi Zhong

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Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images

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arxiv 2007.09695 v1 pith:3AKXVLZW submitted 2020-07-19 eess.IV cs.CVcs.LG

Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images

Yi Zhong This is my paper

classification eess.IV cs.CVcs.LG

keywords covid-19deepimagesconvolutionalimagingmodelneuralopen-source

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The COVID-19 epidemic has become a major safety and health threat worldwide. Imaging diagnosis is one of the most effective ways to screen COVID-19. This project utilizes several open-source or public datasets to present an open-source dataset of COVID-19 CXRs, named COVID-19-CXR-Dataset, and introduces a deep convolutional neural network model. The model validates on 740 test images and achieves 87.3% accuracy, 89.67 % precision, and 84.46% recall, and correctly classifies 98 out of 100 COVID-19 x-ray images in test set with more than 81% prediction probability under the condition of 95% confidence interval. This project may serve as a reference for other researchers aiming to advance the development of deep learning applications in medical imaging.

Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images

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