To Develop a Federated Learning-based Model for Detection and Classification of Diabetic Retinopathy

Diabetic retinopathy (DR) is a serious public health issue that frequently causes diabetic people to lose their eyesight. Early detection is significant for effective management and treatment of patients; however, the traditional centralized approach to medical data analysis poses significant challenges related to patient privacy and data security. This project addresses these challenges by leveraging federated learning. Several healthcare organizations can work together to jointly build a model using this decentralized machine learning technique without disclosing private patient information. The proposed solution involves developing a neural network model that processes retinal images to accurately classify the severity of diabetic retinopathy. The model uses local datasets from many institutions through the use of federated learning, guaranteeing data privacy while preserving excellent performance. The incorporation of cutting-edge strategies like differential privacy further improves the security of patient data. In addition to increasing diagnostic precision, this strategy encourages healthcare providers to work together on research projects.