Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915252782759936 |
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| author | Raj, Gajan Mohan Morley, Michael G. Eslami, Mohammad |
| author_facet | Raj, Gajan Mohan Morley, Michael G. Eslami, Mohammad |
| contents | Diabetic retinopathy is the leading cause of vision loss in working-age adults worldwide, yet under-resourced regions lack ophthalmologists. Current state-of-the-art deep learning systems struggle at these institutions due to limited generalizability. This paper explores a novel federated learning system for diabetic retinopathy diagnosis with the EfficientNetB0 architecture to leverage fundus data from multiple institutions to improve diagnostic generalizability at under-resourced hospitals while preserving patient-privacy. The federated model achieved 93.21% accuracy in five-category classification on an unseen dataset and 91.05% on lower-quality images from a simulated under-resourced institution. The model was deployed onto two apps for quick and accurate diagnosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00869 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions Raj, Gajan Mohan Morley, Michael G. Eslami, Mohammad Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Diabetic retinopathy is the leading cause of vision loss in working-age adults worldwide, yet under-resourced regions lack ophthalmologists. Current state-of-the-art deep learning systems struggle at these institutions due to limited generalizability. This paper explores a novel federated learning system for diabetic retinopathy diagnosis with the EfficientNetB0 architecture to leverage fundus data from multiple institutions to improve diagnostic generalizability at under-resourced hospitals while preserving patient-privacy. The federated model achieved 93.21% accuracy in five-category classification on an unseen dataset and 91.05% on lower-quality images from a simulated under-resourced institution. The model was deployed onto two apps for quick and accurate diagnosis. |
| title | Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.00869 |