Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images

• Published in: Informatics in medicine unlocked Vol. 30; p. 100916
• Main Authors: Hossain, Md. Belal, Iqbal, S.M. Hasan Sazzad, Islam, Md. Monirul, Akhtar, Md. Nasim, Sarker, Iqbal H.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 2022; The Authors. Published by Elsevier Ltd; Elsevier
• Subjects: COVID-19; Deep learning; ResNet50; Transfer learning; Deep learning; COVID-19; Transfer learning; ResNet50
• ISSN: 2352-9148; 2352-9148
• DOI: 10.1016/j.imu.2022.100916

COVID-19 cases are putting pressure on healthcare systems all around the world. Due to the lack of available testing kits, it is impractical for screening every patient with a respiratory ailment using traditional methods (RT-PCR). In addition, the tests have a high turn-around time and low sensitivity. Detecting suspected COVID-19 infections from the chest X-ray might help isolate high-risk people before the RT-PCR test. Most healthcare systems already have X-ray equipment, and because most current X-ray systems have already been computerized, there is no need to transfer the samples. The use of a chest X-ray to prioritize the selection of patients for subsequent RT-PCR testing is the motivation of this work. Transfer learning (TL) with fine-tuning on deep convolutional neural network-based ResNet50 model has been proposed in this work to classify COVID-19 patients from the COVID-19 Radiography Database. Ten distinct pre-trained weights, trained on varieties of large-scale datasets using various approaches such as supervised learning, self-supervised learning, and others, have been utilized in this work. Our proposed iNat2021_Mini_SwAV_1k model, pre-trained on the iNat2021 Mini dataset using the SwAV algorithm, outperforms the other ResNet50 TL models. For COVID instances in the two-class (Covid and Normal) classification, our work achieved 99.17% validation accuracy, 99.95% train accuracy, 99.31% precision, 99.03% sensitivity, and 99.17% F1-score. Some domain-adapted (ImageNet_ChestX−ray14) and in-domain (ChexPert, ChestX-ray14) models looked promising in medical image classification by scoring significantly higher than other models.