Diabetic Retinopathy (DR) stands as the leading cause of blindness globally, particularly affecting individuals between the ages of 20 and 70. This paper presents a Computer-Aided Diagnosis (CAD) system designed for the automatic classification of retinal images into five distinct classes: Normal, Mild, Moderate, Severe, and Proliferative Diabetic Retinopathy (PDR). The proposed system leverages Convolutional Neural Networks (CNNs) employing pre-trained deep learning models. Through the application of fine-tuning techniques, our model is trained on fundus images of diabetic retinopathy with resolutions of 350x350x3 and 224x224x3. Experimental results obtained on the Kaggle platform, utilizing resources comprising 4 CPUs, 17 GB RAM, and 1 GB Disk, demonstrate the efficacy of our approach. The achieved Area Under the Curve (AUC) values for CNN, MobileNet, VGG-16, InceptionV3, and InceptionResNetV2 models are 0.50, 0.70, 0.53, 0.63, and 0.69, respectively.

翻译：糖尿病视网膜病变（DR）是全球范围内导致失明的主要原因，尤其影响20至70岁人群。本文提出了一种计算机辅助诊断（CAD）系统，旨在将视网膜图像自动分类为五个不同类别：正常、轻度、中度、重度和增殖性糖尿病视网膜病变（PDR）。该系统利用卷积神经网络（CNN）并采用预训练深度学习模型。通过微调技术的应用，我们的模型在分辨率为350x350x3和224x224x3的糖尿病视网膜病变眼底图像上进行训练。在Kaggle平台上使用4个CPU、17 GB RAM和1 GB磁盘资源获得的实验结果表明了该方法的有效性。CNN、MobileNet、VGG-16、InceptionV3和InceptionResNetV2模型实现的曲线下面积（AUC）值分别为0.50、0.70、0.53、0.63和0.69。