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

arXiv: https://arxiv.org/abs/2501.12048 · HTML: https://mlsanity.com/paper/2501.12048/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2025.

## Built on by (most cited first, 1 of 1)

- [Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overview](https://mlsanity.com/paper/2604.02448/) (2026)

## Abstract (arXiv)

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.