Abstract
In this study, a custom Vision Transformer is used for classifying abnormal fundus images and differentiating them from normal ones. The abnormality in images might be due to any of the following six ocular diseases: age-related macular degeneration, cataracts, diabetes, glaucoma, hypertension, and myopia. Three different Vision Transformer architectures with 8, 14, and 24 layers have been used for the classification problem to identify the optimum one. The entire dataset is classified into seven different labels—healthy and six different diseases. The proposed implementation improves on the existing F1-score, precision, sensitivity, and Kappa scores of ocular disease identification presenting a maximum F1-score of 83.49% with 84% sensitivity, 83% precision, and 0.802 Kappa score using Vision Transformer-14.
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Gummadi, S.D., Ghosh, A. (2023). Classification of Ocular Diseases: A Vision Transformer-Based Approach. In: Roy, S., Sinwar, D., Dey, N., Perumal, T., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision. ICICV 2022. Lecture Notes in Networks and Systems, vol 680. Springer, Singapore. https://doi.org/10.1007/978-981-99-2602-2_25
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DOI: https://doi.org/10.1007/978-981-99-2602-2_25
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