Accurate and speedy detection of COVID-19 is essential to curb the spread of the disease and avoid overwhelming the health care system. COVID-19 detection using X-ray images is commonly practiced at medical centers; however, it requires the intervention of medical professionals trained in diagnosing and interpreting medical imagining. In this paper, we employ deep transfer learning models to detect COVID-19 on a dataset of over 20,000 X-ray images. Our results on 5 pretrained models (VGG19, InceptionV3, MobileNetV2, DenseNet121, and ResNet101V2) show high performance of 99% without image augmentation, and 93\% when image augmentation is used.
Alsakran, Jamal; Alnemer, Loai; Alhindawi, Nouh; and Muard, Omayya
"Detecting COVID-19 in X-ray Images using Transfer Learning,"
Information Sciences Letters: Vol. 11
, PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol11/iss5/38