用残差网络和DCGAN增强图像，自动识别糖尿病视网膜病变五级
Diabetic Retinopathy Detection Using CNN with Residual Block with DCGAN
- 残差结构提升特征提取能力，配合DCGAN生成多样化训练图像
- 在五类分级任务中实现高准确率，提升模型对真实图像的泛化性
- 适合医疗资源不足地区的大规模筛查，助力早期诊断
糖尿病视网膜病变（DR）是全球致盲主因之一，由糖尿病引起的视网膜血管损伤所致。早期检测与分类对及时干预、防止视力丧失至关重要。本文提出一种基于卷积神经网络（CNN）与残差块架构的自动化DR检测系统，有效提升特征提取能力与模型性能。为进一步增强模型鲁棒性，采用深度卷积生成对抗网络（DCGAN）进行数据增强，生成多样化的视网膜图像，增加训练数据的变异性，使模型更适应真实世界中的图像差异。系统可将视网膜图像分为五类：无DR至增生性DR，提供高效、可扩展的早期诊断与疾病进展监测方案。该模型旨在支持医疗专业人员在资源受限环境下开展大规模筛查。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR) is a major cause of blindness worldwide, caused by damage to the blood vessels in the retina due to diabetes. Early detection and classification of DR are crucial for timely intervention and preventing vision loss. This work proposes an automated system for DR detection using Convolutional Neural Networks (CNNs) with a residual block architecture, which enhances feature extraction and model performance. To further improve the model's robustness, we incorporate advanced data augmentation techniques, specifically leveraging a Deep Convolutional Generative Adversarial Network (DCGAN) for generating diverse retinal images. This approach increases the variability of training data, making the model more generalizable and capable of handling real-world variations in retinal images. The system is designed to classify retinal images into five distinct categories, from No DR to Proliferative DR, providing an efficient and scalable solution for early diagnosis and monitoring of DR progression. The proposed model aims to support healthcare professionals in large-scale DR screening, especially in resource-constrained settings.
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