Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification
Kidney diseases are becoming an alarming concern around the globe. Premature diagnosis of kidney disease can save precious human lives by taking preventive measures. Deep learning demonstrates a substantial performance in various medical disciplines. Numerous deep learning approaches are suggested i...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-04-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2800.pdf |
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| author | Zeshan Aslam Khan Muhammad Waqar Hashir Ullah Khan Naveed Ishtiaq Chaudhary Abeer TMA Khan Iqra Ishtiaq Farrukh Aslam Khan Muhammad Asif Zahoor Raja |
| author_facet | Zeshan Aslam Khan Muhammad Waqar Hashir Ullah Khan Naveed Ishtiaq Chaudhary Abeer TMA Khan Iqra Ishtiaq Farrukh Aslam Khan Muhammad Asif Zahoor Raja |
| author_sort | Zeshan Aslam Khan |
| collection | DOAJ |
| description | Kidney diseases are becoming an alarming concern around the globe. Premature diagnosis of kidney disease can save precious human lives by taking preventive measures. Deep learning demonstrates a substantial performance in various medical disciplines. Numerous deep learning approaches are suggested in the literature for accurate chronic kidney disease classification by compromising on architectural complexity, classification speed, and resource constraints. In this study, deep transfer learning is exploited by incorporating unexplored yet effective variants of ConvNeXt and EfficientNetV2 for accurate and efficient classification of chronic kidney diseases. The benchmark computed tomography (CT)-based kidney database containing 12,446 CT scans of kidney tumor, stone cysts, and normal patients is utilized to train the designed fine-tuned networks. However, due to the highly imbalanced distribution of images among classes, the operation of data trimming is exploited for balancing the number of CT scans in each class, which is essential for designing an unbiased predictive network. By utilizing fine-tuned pre-trained models for our specific task, the training time is reduced leading to a computationally inexpensive solution. After the comprehensive hyperparameters tuning with respect to changes in learning rates, batch sizes, and optimizers, it is depicted that the designed fine-tuned EfficientNetV2B0 network of 23.8 MB in size with only 6.2 million architectural parameters shows substantial diagnostic performance by achieving a generalized test accuracy of 99.75% on balanced CT kidney database. Furthermore, the designed fine-tuned EfficientNetV2B0 attains high precision, recall, and F1-score of 99.75%, 99.63%, and 99.75%, respectively. Moreover, the final fine-tuned EfficientNetV2B0 ensures its scalability by achieving an impressive diagnostic accuracy of 99.73% on the test set of the original CT kidney dataset as well. Through the extensive evaluation of the proposed transfer learning strategy, it is concluded that the proposed design of fine-tuned EfficientNetV2B0 outperforms its counterparts in terms of accuracy and computational efficiency for chronic kidney disease diagnosis tasks. The final fine-tuned EfficientNetV2B0 serves as an accurate, efficient, and computationally inexpensive solution tailored for real-time deployment on medical or mobile edge devices. |
| format | Article |
| id | doaj-art-b63e1292366c406b97bc5e5488d027bf |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ Computer Science |
| spelling | doaj-art-b63e1292366c406b97bc5e5488d027bf2025-08-20T02:16:33ZengPeerJ Inc.PeerJ Computer Science2376-59922025-04-0111e280010.7717/peerj-cs.2800Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classificationZeshan Aslam Khan0Muhammad Waqar1Hashir Ullah Khan2Naveed Ishtiaq Chaudhary3Abeer TMA Khan4Iqra Ishtiaq5Farrukh Aslam Khan6Muhammad Asif Zahoor Raja7Electrical and Computer Engineering, International Islamic University, Islamabad, PakistanInternational Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Yunlin, TaiwanInternational Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Yunlin, TaiwanFuture Technology Research Center, National Yunlin University of Science and Technology, Yunlin, TaiwanRawal Institute of Health Sciences, Rawalpindi, PakistanTechnological University Dublin, Dublin, IrelandCenter of Excellence in Information Assurance, King Saud University, Riyadh, Saudi ArabiaFuture Technology Research Center, National Yunlin University of Science and Technology, Yunlin, TaiwanKidney diseases are becoming an alarming concern around the globe. Premature diagnosis of kidney disease can save precious human lives by taking preventive measures. Deep learning demonstrates a substantial performance in various medical disciplines. Numerous deep learning approaches are suggested in the literature for accurate chronic kidney disease classification by compromising on architectural complexity, classification speed, and resource constraints. In this study, deep transfer learning is exploited by incorporating unexplored yet effective variants of ConvNeXt and EfficientNetV2 for accurate and efficient classification of chronic kidney diseases. The benchmark computed tomography (CT)-based kidney database containing 12,446 CT scans of kidney tumor, stone cysts, and normal patients is utilized to train the designed fine-tuned networks. However, due to the highly imbalanced distribution of images among classes, the operation of data trimming is exploited for balancing the number of CT scans in each class, which is essential for designing an unbiased predictive network. By utilizing fine-tuned pre-trained models for our specific task, the training time is reduced leading to a computationally inexpensive solution. After the comprehensive hyperparameters tuning with respect to changes in learning rates, batch sizes, and optimizers, it is depicted that the designed fine-tuned EfficientNetV2B0 network of 23.8 MB in size with only 6.2 million architectural parameters shows substantial diagnostic performance by achieving a generalized test accuracy of 99.75% on balanced CT kidney database. Furthermore, the designed fine-tuned EfficientNetV2B0 attains high precision, recall, and F1-score of 99.75%, 99.63%, and 99.75%, respectively. Moreover, the final fine-tuned EfficientNetV2B0 ensures its scalability by achieving an impressive diagnostic accuracy of 99.73% on the test set of the original CT kidney dataset as well. Through the extensive evaluation of the proposed transfer learning strategy, it is concluded that the proposed design of fine-tuned EfficientNetV2B0 outperforms its counterparts in terms of accuracy and computational efficiency for chronic kidney disease diagnosis tasks. The final fine-tuned EfficientNetV2B0 serves as an accurate, efficient, and computationally inexpensive solution tailored for real-time deployment on medical or mobile edge devices.https://peerj.com/articles/cs-2800.pdfTransfer learningDeep neural networksKidney diseaseDisease diagnosis |
| spellingShingle | Zeshan Aslam Khan Muhammad Waqar Hashir Ullah Khan Naveed Ishtiaq Chaudhary Abeer TMA Khan Iqra Ishtiaq Farrukh Aslam Khan Muhammad Asif Zahoor Raja Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification PeerJ Computer Science Transfer learning Deep neural networks Kidney disease Disease diagnosis |
| title | Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification |
| title_full | Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification |
| title_fullStr | Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification |
| title_full_unstemmed | Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification |
| title_short | Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification |
| title_sort | fine tuned deep transfer learning an effective strategy for the accurate chronic kidney disease classification |
| topic | Transfer learning Deep neural networks Kidney disease Disease diagnosis |
| url | https://peerj.com/articles/cs-2800.pdf |
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