A Privacy-Preserving Approach Using Deep Learning Models for Diabetic Retinopathy Diagnosis
Document Type
Article
Publication Date
1-1-2024
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
Artificial intelligence, Blockchains, Feature extraction, Predictive models, Privacy, Diabetic retinopathy, Deep learning, Data models, Convolutional neural networks, Blindness, Convolutional neural network, deep learning, diabetic retinopathy, encryption, privacy-preserving
Divisions
biomedengine
Funders
Xuzhou Science and Technology Project (KC21182),Unveiling and Leading Project of Xuzhou Medical University (XZHMU) (JBGS202204),Universiti Malaya under the University Grant-Partnership (MG004-2024)
Publication Title
IEEE Access
Volume
12
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA