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AN EXPLAINABLE VISION TRANSFORMER-BASED FRAMEWORK FOR RETINAL DISEASES CLASSIFICATION
Authors
Keerthi Guttikonda,Siri Chandana Rachamsetti,Srinu Vakada,Krishna Sai Seera,,Gayathri Devi Vaddi
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Keywords
Retinal disease classification, Vision Transformer, Fundus imaging, Computer-aided diagnosis, Explainable AI, Telemedicine.
Abstract
Early and effective identification of retinal pathologies plays a very crucial role in the prevention of irreversible vision loss and facilitating the process of large-scale retinal screenings. This study focuses on the implementation of an explainable and Vision Transformer-based multi-class retinal disease classifiers using color fundus photographs, focusing on the proposed work around the Swin Transformer-Base architecture. A selectively compiled dataset of 4,217 retinal fundus photographs was assembled by combining various publicly accessible sources, followed by the classification of the compiled set into four retinal disease classes: Normal, Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Branch Retinal Vein Occlusion (BRVO) diseased retinas. The previously mentioned Swin Transformer-Base network, pretrained on large-scale image datasets, was further adapted by the use of transfer learning to achieve more accurate results through standardized preprocessing and optimizing settings during the training process to avoid any potential bias in the derived results. From the experimental analysis, the proposed work showcases the potential of the proposed Swin Transformer-Base architecture in successfully maximizing the classification accuracy to 99.54%, and to further provide more robustness and explainability to the proposed results, the implementation of Gradient-weighted Class Activation Mapping (Grad-CAM) visualization techniques has been accomplished, focusing on the identification of critical retinal areas in retinal pathologies, thereby facilitating more enhanced clinical usability. Furthermore, to provide more utility value to the proposed clinical approach, a lightweight and easy-to-use diagnostic tool has been provided to upload the images in real time and to access the results in a more facile graphical format.
IJCRT's Publication Details
Unique Identification Number - IJCRT2603697
Paper ID - 303563
Author type - Indian Author
Page Number(s) - f835-f841
Pubished in - Volume 14 | Issue 3 | March 2026
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No Of Downloads - 48
Author Country - India, -, -, -, -, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Keerthi Guttikonda,Siri Chandana Rachamsetti,Srinu Vakada,Krishna Sai Seera,,Gayathri Devi Vaddi, "AN EXPLAINABLE VISION TRANSFORMER-BASED FRAMEWORK FOR RETINAL DISEASES CLASSIFICATION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 3, pp.f835-f841, March 2026, Available at :http://www.ijcrt.org/papers/IJCRT2603697.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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