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

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>i</mml:mi> <mml:mi>N</mml:mi> <mml:mi>a</mml:mi> <mml:mi>t</mml:mi> <mml:mn>2021</mml:mn> <mml:mtext>_</mml:mtext> <mml:mi>M</mml:mi> <mml:mi>i</mml:mi> <mml:mi>n</mml:mi> <mml:mi>i</mml:mi> <mml:mtext>_</mml:mtext> <mml:mi>S</mml:mi> <mml:mi>w</mml:mi> <mml:mi>A</mml:mi> <mml:mi>V</mml:mi> <mml:mtext>_</mml:mtext> <mml:mn>1</mml:mn> <mml:mi>k</mml:mi></mml:mrow> </mml:math> 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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>I</mml:mi> <mml:mi>m</mml:mi> <mml:mi>a</mml:mi> <mml:mi>g</mml:mi> <mml:mi>e</mml:mi> <mml:mi>N</mml:mi> <mml:mi>e</mml:mi> <mml:mi>t</mml:mi> <mml:mtext>_</mml:mtext> <mml:mi>C</mml:mi> <mml:mi>h</mml:mi> <mml:mi>e</mml:mi> <mml:mi>s</mml:mi> <mml:mi>t</mml:mi> <mml:mi>X</mml:mi> <mml:mo>-</mml:mo> <mml:mi>r</mml:mi> <mml:mi>a</mml:mi> <mml:mi>y</mml:mi> <mml:mn>14</mml:mn></mml:mrow> </mml:math> ) and in-domain (ChexPert, ChestX-ray14) models looked promising in medical image classification by scoring significantly higher than other models.