  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.
