Vidivelli S, Padmakumari P, Parthiban C, DharunBalaji A, Manikandan R, Gandomi Amir H
School of Computing, SASTRA University, Thanjavur, Tamilnadu, India.
Department of CSE, SASTRA University, Thanjavur, Tamilnadu, India.
Sci Rep. 2025 Jan 24;15(1):3115. doi: 10.1038/s41598-024-75867-3.
Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological diseases. Disorders such as hypertension, glaucoma, and diabetic retinopathy are indicated by structural alterations in the optic disc, blood vessels, fovea, and macula. Patients frequently deal with various ophthalmological conditions in either one or both eyes. In this article, we have used different deep learning models for the categorisation of ophthalmological disorders into multiple classes and multiple labels utilising transfer learning-based convolutional neural network (CNN) methods. The Ocular Disease Intelligent Recognition (ODIR) database is used for experiments, and it contains fundus images of the patient's left and right eyes. We compared the performance of two different optimisers, Stochastic Gradient Descent (SGD) and Adam, separately. The best result was achieved using the MobileNet model with the Adam optimiser, yielding a testing accuracy of 89.64%.
眼底成像作为一种记录视网膜结构成分和异常情况的技术，对于观察和识别眼科疾病至关重要。高血压、青光眼和糖尿病性视网膜病变等疾病可通过视盘、血管、黄斑中心凹和黄斑的结构改变来指示。患者经常单眼或双眼患有各种眼科疾病。在本文中，我们使用了不同的深度学习模型，利用基于迁移学习的卷积神经网络（CNN）方法将眼科疾病分类为多个类别和多个标签。使用眼部疾病智能识别（ODIR）数据库进行实验，该数据库包含患者左眼和右眼的眼底图像。我们分别比较了两种不同优化器随机梯度下降（SGD）和Adam的性能。使用带有Adam优化器的MobileNet模型取得了最佳结果，测试准确率为89.64%。
