Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images
This work addresses the need for effective screening tools for COVID-19, but it is incremental as it applies existing deep learning methods to a new medical dataset.
The researchers tackled the problem of diagnosing COVID-19 from chest X-ray images by creating an open-source dataset and a deep convolutional neural network model, achieving 87.3% accuracy and correctly classifying 98 out of 100 COVID-19 images in the test set.
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.