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 sensitiv...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235291482200065X |
| _version_ | 1828791283729039360 |
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| author | Md. Belal Hossain S.M. Hasan Sazzad Iqbal Md. Monirul Islam Md. Nasim Akhtar Iqbal H. Sarker |
| author_facet | Md. Belal Hossain S.M. Hasan Sazzad Iqbal Md. Monirul Islam Md. Nasim Akhtar Iqbal H. Sarker |
| author_sort | Md. Belal Hossain |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T02:48:07Z |
| format | Article |
| id | doaj.art-d2b5b1e16b1345d4937f28ae67968655 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-12-12T02:48:07Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-d2b5b1e16b1345d4937f28ae679686552022-12-22T00:40:58ZengElsevierInformatics in Medicine Unlocked2352-91482022-01-0130100916Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray imagesMd. Belal Hossain0S.M. Hasan Sazzad Iqbal1Md. Monirul Islam2Md. Nasim Akhtar3Iqbal H. Sarker4Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna 6600, BangladeshDepartment of Computer Science and Engineering, Pabna University of Science and Technology, Pabna 6600, BangladeshDepartment of Textile Engineering, Uttara University, Dhaka 1230, BangladeshDepartment of Computer Science and Engineering, Dhaka University of Engineering Technology, Gazipur, 1707, BangladeshDepartment of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S235291482200065XDeep learningTransfer learningResNet50COVID-19 |
| spellingShingle | Md. Belal Hossain S.M. Hasan Sazzad Iqbal Md. Monirul Islam Md. Nasim Akhtar Iqbal H. Sarker Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images Informatics in Medicine Unlocked Deep learning Transfer learning ResNet50 COVID-19 |
| title | Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images |
| title_full | Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images |
| title_fullStr | Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images |
| title_full_unstemmed | Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images |
| title_short | Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images |
| title_sort | transfer learning with fine tuned deep cnn resnet50 model for classifying covid 19 from chest x ray images |
| topic | Deep learning Transfer learning ResNet50 COVID-19 |
| url | http://www.sciencedirect.com/science/article/pii/S235291482200065X |
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