DeepCOVIDNet-CXR: Deep learning strategies for identifying COVID-19 on enhanced chest X-rays

Abstract

COVID-19 is one of the recent major epidemics, which accelerates its mortality and prevalence worldwide. Most literature on chest X-ray-based COVID-19 analysis has focused on multi-case classification (COVID-19, pneumonia, and normal) by the advantages of Deep Learning. However, the limited number of chest X-rays with COVID-19 is a prominent deficiency for clinical relevance. This study aims at evaluating COVID-19 identification performances using adaptive histogram equalization (AHE) to feed the ConvNet architectures with reliable lung anatomy of airways. We experimented with balanced small-and large-scale COVID-19 databases using left lung, right lung, and complete chest X-rays with various AHE parameters. On multiple strategies, we applied transfer learning on four ConvNet architectures (MobileNet, DarkNet19, VGG16, and AlexNet). Whereas DarkNet19 reached the highest multi-case identification performance with an accuracy rate of 98.26% on the small-scale dataset, VGG16 achieved the best generalization performance with an accuracy rate of 95.04% on the large-scale dataset. Our study is one of the pioneering approaches that analyses 3615 COVID-19 cases and specifies the most responsible AHE parameters for ConvNet architectures in the multi-case classification.