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.

翻译：全球范围内眼部疾病的患病率持续上升，构成了重大的公共卫生挑战。早期检测和及时干预对于预防视力损伤及改善患者预后至关重要。本研究提出了一种名为"有限数据下的类别扩展"的新框架，用于训练能够对视网膜眼底图像进行分类的分类器。该分类器首先被训练以识别与健康类别和糖尿病视网膜病变类别相关的特征，随后通过微调适应将输入图像分类为健康、糖尿病视网膜病变和青光眼三个类别的任务。这一策略使模型能够逐步提升其分类能力，在仅有有限标注数据集可用的场景下尤为有益。研究中还采用了扰动方法以识别影响模型决策过程的输入图像特征。我们在公开数据集上实现了91%的整体准确率。