<![CDATA[Diabetic retinopathy (DR) is an eye condition caused by damage to the blood vessels of the retina due to high blood sugar levels, commonly associated with diabetes. Without proper treatment, it can lead to visual impairment or blindness. Traditional machine learning (ML) approaches for detecting Diabetic retinopathy rely on centralized data aggregation, which raises significant privacy concerns and often encounters regulatory challenges. To address these issues, the DR-FEDPAM model is proposed for the detection of diabetic retinopathy. Initially, the images are preprocessed using a Median Filter (MeF) and Gaussian Star Filter (GaSF) to reduce noise and enhance image quality. The preprocessed images are then input into a federated proximal model. Federated Learning (FL) enables multiple local models to train on distributed devices without sharing raw data. After the local models process the data, their parameters are aggregated through a Global Federated Averaging (GFA) model. This global model combines the parameters from all local models to produce a unified model that classifies each image as either normal or diabetic retinopathy. The model’s performance is evaluated using precision (PR), F1-score (F1), specificity (SP), recall (RE), and accuracy (AC). The DR-FEDPAM achieves a balanced trade-off with 7.8 million parameters, 1.7 FLOPs, and an average inference time of 13.9 ms. The model improves overall accuracy by 5.44%, 1.89%, and 4.43% compared to AlexNet, ResNet, and APSO, respectively. Experimental results show that the proposed method achieves an accuracy of 98.36% in detecting DR]]>
Copyrights © 2025
