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arXiv · 2501.12048

Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye

Shramana Dey·Pallabi Dutta·Riddhasree Bhattacharyya·Surochita Pal·Sushmita Mitra·Rajiv Raman
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Abstract

The prevalence of ocular illnesses is growing globally, presenting a substantial public health challenge. Early detection and timely intervention are crucial for averting visual impairment and enhancing patient prognosis. This research introduces a new framework called Class Extension with Limited Data (CELD) to train a classifier to categorize retinal fundus images. The classifier is initially trained to identify relevant features concerning Healthy and Diabetic Retinopathy (DR) classes and later fine-tuned to adapt to the task of classifying the input images into three classes: Healthy, DR, and Glaucoma. This strategy allows the model to gradually enhance its classification capabilities, which is beneficial in situations where there are only a limited number of labeled datasets available. Perturbation methods are also used to identify the input image characteristics responsible for influencing the models decision-making process. We achieve an overall accuracy of 91% on publicly available datasets.

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Shramana Dey, Pallabi Dutta, Riddhasree Bhattacharyya, Surochita Pal, Sushmita Mitra, Rajiv Raman. 2025-01-21. Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye. https://doi.org/10.1007/978-3-031-78104-9_9

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