Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography
Abstract Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gath...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-15634-4 |
| _version_ | 1811224246103310336 |
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| author | Md Nazmul Islam Mehedi Hasan Md. Kabir Hossain Md. Golam Rabiul Alam Md Zia Uddin Ahmet Soylu |
| author_facet | Md Nazmul Islam Mehedi Hasan Md. Kabir Hossain Md. Golam Rabiul Alam Md Zia Uddin Ahmet Soylu |
| author_sort | Md Nazmul Islam |
| collection | DOAJ |
| description | Abstract Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community’s research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones. |
| first_indexed | 2024-04-12T08:45:11Z |
| format | Article |
| id | doaj.art-89e1e16ea8124241bedb856115b65d64 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T08:45:11Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-89e1e16ea8124241bedb856115b65d642022-12-22T03:39:43ZengNature PortfolioScientific Reports2045-23222022-07-0112111410.1038/s41598-022-15634-4Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiographyMd Nazmul Islam0Mehedi Hasan1Md. Kabir Hossain2Md. Golam Rabiul Alam3Md Zia Uddin4Ahmet Soylu5Department of Computer Science and Engineering, BRAC UniversityRadiology & Imaging Technology, Bangladesh University of Health SciencesDepartment of Nephrology, Bangabandhu Sheikh Mujib Medical UniversityDepartment of Computer Science and Engineering, BRAC UniversitySoftware and Service Innovation, SINTEF DigitalDepartment of Computer Science, Norwegian University of Science and TechnologyAbstract Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community’s research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones.https://doi.org/10.1038/s41598-022-15634-4 |
| spellingShingle | Md Nazmul Islam Mehedi Hasan Md. Kabir Hossain Md. Golam Rabiul Alam Md Zia Uddin Ahmet Soylu Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography Scientific Reports |
| title | Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography |
| title_full | Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography |
| title_fullStr | Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography |
| title_full_unstemmed | Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography |
| title_short | Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography |
| title_sort | vision transformer and explainable transfer learning models for auto detection of kidney cyst stone and tumor from ct radiography |
| url | https://doi.org/10.1038/s41598-022-15634-4 |
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