Ng, Jun Chen and Yeoh, Pauline Shan Qing and Bing, Li and Wu, Xiang and Hasikin, Khairunnisa and Lai, Khin Wee (2024) A Privacy-Preserving Approach Using Deep Learning Models for Diabetic Retinopathy Diagnosis. IEEE Access, 12. pp. 145159-145173. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3469537.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| 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) |
| Uncontrolled 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 |
| Subjects: | R Medicine > R Medicine (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 03 Jan 2025 08:44 |
| Last Modified: | 03 Jan 2025 08:44 |
| URI: | http://eprints.um.edu.my/id/eprint/47078 |
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