Classification of Diabetic Retinopathy using Pre-Trained Deep Learning
  Models
Inas Al-Kamachy Prof. Dr. Reza Hassanpour Prof. Roya Choupani
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.