Journal Article

EdgeSVDNet: 5G-Enabled Detection and Classification of Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Baig, Talha Imtiaz, Shafiq, Muhammad, Liu, Xiaowen, Bilal, Anas, Long, Haixia

2023

Overview

The rise of vision-threatening diabetic retinopathy (VTDR) underscores the imperative for advanced and efficient early detection mechanisms. With the integration of the Internet of Things (IoT) and 5G technologies, there is transformative potential for VTDR diagnosis, facilitating real-time processing of the burgeoning volume of fundus images (FIs). Combined with artificial intelligence (AI), this offers a robust platform for managing vast healthcare datasets and achieving unparalleled disease detection precision. Our study introduces a novel AI-driven VTDR detection framework that integrates multiple models through majority voting. This comprehensive approach encompasses pre-processing, data augmentation, feature extraction using a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model, and classification through an enhanced SVM-RBF combined with a decision tree (DT) and K-nearest neighbor (KNN). Validated on the IDRiD dataset, our model boasts an accuracy of 99.89%, a sensitivity of 84.40%, and a specificity of 100%, marking a significant improvement over the traditional method. The convergence of the IoT, 5G, and AI technologies herald a transformative era in healthcare, ensuring timely and accurate VTDR diagnoses, especially in geographically underserved regions.

• Publisher: MDPI AG
• Subject: Accuracy / Artificial intelligence / Artificial neural networks / Business metrics / Cancer / Classification / Data augmentation / Datasets / Decision trees / Deep learning / Diabetes / Diabetic retinopathy / Disease / Feature extraction / Health care / Internet of Things / Literature reviews / Machine learning / Medical imaging / Methods / Model accuracy / Neural networks / Singular value decomposition / Support vector machines