Journal Article

Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification

Shafiq, Muhammad, Liu, Xiaowen, Imran, Azhar, Baig, Talha Imtiaz, Bilal, Anas, Long, Haixia, Alzahrani, Abdulkareem

2024

Overview

The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach. Addressing this, our study introduces a novel methodology that amplifies the robustness and precision of the detection process. This is complemented by the groundbreaking Hierarchical Block Attention (HBA) and HBA-U-Net architecture, which notably propel attention mechanisms in image segmentation. This innovative model refines image processing without imposing excessive computational demands by honing in on individual pixel intricacies, spatial relationships, and channel-specific attention. Building upon this innovation, our proposed method employs a multi-stage strategy encompassing data pre-processing, feature extraction via a hybrid CNN-SVD model, and classification employing an amalgamation of Improved Support Vector Machine-Radial Basis Function (ISVM-RBF), DT, and KNN techniques. Rigorously tested on the IDRiD dataset classified into five severity tiers, the hybrid model yields remarkable performance, achieving a 99.18% accuracy, 98.15% sensitivity, and 100% specificity in VTDR detection, thus surpassing existing methods. These results underscore a more potent avenue for diagnosing and addressing this crucial ocular condition while underscoring AI’s transformative potential in medical care, particularly in ophthalmology.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Accuracy / Analysis / Artificial Intelligence / Automation / Biology and Life Sciences / Blood vessels / Care and treatment / Classification / Computer and Information Sciences / Datasets / Deep learning / Diabetes / Diabetes Mellitus / Diabetic retinopathy / Diabetic Retinopathy - diagnostic imaging / Diagnosis / Diagnosis, Computer-Assisted - methods / Disease / Engineering and Technology / Equipment and supplies / Feature extraction / Glaucoma / Honing / Humans / Image Interpretation, Computer-Assisted - methods / Image processing / Image segmentation / Literature reviews / Machine learning / Macular degeneration / Medical tests / Medicine and Health Sciences / Methods / Neural networks / Ophthalmology / Physical Sciences / Radial basis function / Research methodology / Retina / Retinal images / Retinopathy / Support Vector Machine / Support vector machines / Technology application / Vision / Visual impairment