Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework

Sakib, S.; Siddique, M. A. B.; Khan, M. M. R.; Yasmin, N.; Aziz, A.; Chowdhury, M.; Tasawar, I. K.

The world economy as well as public health have been facing a devastating effect caused by the disease termed Coronavirus (COVID-19). A significant step of COVID-19 affected patient's treatment is the faster and accurate detection of the disease which is the motivation of this study. In this paper, the implementation of a deep transfer learning-based framework using a pre-trained network (ResNet-50) for detecting COVID-19 from the chest X-rays was done. Our dataset consists of 2905 chest X-ray images of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). The implemented neural network demonstrates significant performance in classifying the cases with an overall accuracy of 96%. Most importantly, the model has shown significantly good performance over the current research-based methods in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Recall = 1.00, F1-score = 1.00, and Specificity = 1.00). Therefore, our proposed approach can be adapted as a reliable method for faster and accurate COVID-19 affected case detection.