A Privacy-Preserving Approach Using Deep Learning Models for Diabetic Retinopathy Diagnosis

Abstract

Diabetic Retinopathy (DR) is the most common complication of Diabetes Mellitus and can lead to blindness if not detected early. Since DR is often asymptomatic in its early stage, timely diagnosis is crucial. Artificial Intelligence (AI) has the potential to facilitate early disease detection and treatment, but its implementation in the medical field raises significant privacy concerns. The sensitive nature of healthcare data, which includes personal information and medical history, makes data privacy a critical issue. This paper explores the implementation of AI models to predict DR risks while incorporating common defense algorithms to enhance data privacy. An unstructured dataset, specifically the DDR dataset, was used to train Deep Learning (DL) models. Two families of DL models, ResNets and DenseNets, were trained and evaluated based on the performance metrics. ResNet 50 and DenseNet 169 demonstrated superior performance and were selected for further privacy enhancement using encryption. The results indicated that privacy-preserving methods, particularly encryption, did not significantly impact the model performance. In summary, this paper highlights the potential of privacy-preserving AI in predicting the risks of DR.

Keywords

• Convolutional neural network
• deep learning
• diabetic retinopathy
• encryption
• privacy-preserving