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

Informatics in Medicine Unlocked

Iqbal H. Sarker¹  Md. Nasim Akhtar²  Md. Belal Hossain³  S.M. Hasan Sazzad Iqbal³  Md. Monirul Islam⁴ 

[1] Department of Computer Science and Engineering, Chittagong University of Engineering &Department of Computer Science and Engineering, Dhaka University of Engineering Technology, Gazipur, 1707, Bangladesh;Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh;Department of Textile Engineering, Uttara University, Dhaka 1230, Bangladesh;

关键词: Deep learning;    Transfer learning;    ResNet50;    COVID-19;   

DOI  :  

来源: DOAJ

【 摘 要 】

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_1kmodel, 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.

【 授权许可】

Unknown