Abstract
Retinal diseases such as Diabetic Retinopathy (DR) and Macular Hole (MH) significantly impact vision and affect millions worldwide. Early detection is crucial, as DR, a complication of diabetes, damages retinal blood vessels, potentially leading to blindness, while MH disrupts central vision, affecting tasks like reading and facial recognition. This paper employed two lightweight and efficient Convolution Neural Network architectures, MobileNet and NASNetMobile, for the classification of Normal, DR, and MH retinal images. The models were trained on the RFMiD dataset, consisting of 3,200 fundus images, after undergoing preprocessing steps such as resizing, normalization, and augmentation. To address data scarcity, this study leveraged transfer learning and data augmentation techniques, enhancing model generalization and performance. The experimental results demonstrate that MobileNetV2 achieved the highest accuracy of 90.8%, outperforming NASNetMobile, which achieved 89.5% accuracy. These findings highlight the effectiveness of CNNs in retinal disease classification, providing a foundation for AI-assisted ophthalmic diagnosis and early intervention.
Abstract (translated)
视网膜疾病，如糖尿病性视网膜病变（DR）和黄斑孔（MH），对视力有显著影响，并且影响了全球数百万人。早期检测至关重要，因为作为糖尿病并发症的DR会损害视网膜血管，可能导致失明；而MH则干扰中央视野，影响阅读和面部识别等任务。本文采用两种轻量高效的卷积神经网络架构——MobileNet 和 NASNetMobile，用于正常、DR 和 MH 视网膜图像分类。模型在经过预处理步骤（如调整大小、归一化和增强）后的RFMiD数据集上进行训练，该数据集包含3,200张眼底图像。为解决数据稀缺问题，本研究利用了迁移学习和技术数据增强技术，提高了模型的泛化能力和性能。实验结果表明，MobileNetV2 达到了最高的准确率90.8%，超过了NASNetMobile 的89.5% 准确率。这些发现强调了CNN在视网膜疾病分类中的有效性，为人工智能辅助眼科诊断和早期干预奠定了基础。
URL
https://arxiv.org/abs/2506.03186
