Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye
Journal:
arXiv
Published Date:
Jan 21, 2025
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.
