标题： 基于带有残差块的DCGAN的卷积神经网络在糖尿病视网膜病变检测中的应用 显示英文标题

标题： Diabetic Retinopathy Detection Using CNN with Residual Block with DCGAN

摘要： 糖尿病视网膜病变（DR）是全球失明的主要原因，由于糖尿病导致视网膜中的血管受损。早期检测和分类DR对于及时干预和预防视力丧失至关重要。本工作提出了一种使用具有残差块结构的卷积神经网络（CNNs）进行DR检测的自动化系统，该结构增强了特征提取和模型性能。为了进一步提高模型的鲁棒性，我们结合了先进的数据增强技术，具体来说，利用深度卷积生成对抗网络（DCGAN）生成多样化的视网膜图像。这种方法增加了训练数据的多样性，使模型更具泛化能力，并能够处理视网膜图像中的现实世界变化。该系统旨在将视网膜图像分为五个不同的类别，从无DR到增生性DR，为DR的早期诊断和监测提供高效且可扩展的解决方案。所提出的模型旨在支持医疗专业人员在资源有限的环境中进行大规模DR筛查。 显示英文摘要

摘要： 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.