ArticlePloS one2024
Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Early retinal disease detection from fundus images using deep neural networks.Scientific reports · 2026Article
- An enhanced diabetic retinopathy detection approach using optimized deep learning technique.Scientific reports · 2026Article
- Detection of diabetic retinopathy using multicolor image by multimodal network incorporating information bottleneck (MNIIB).Scientific reports · 2025Article
- Hybrid quantum-classical deep learning framework for balanced multiclass diabetic retinopathy classification.MethodsX · 2025Article
- Retinal image-based disease classification using hybrid deep architecture with improved image features.International ophthalmology · 2025Article
- Ensemble-based eye disease detection system utilizing fundus and vascular structures.Scientific reports · 2025Article
- QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning.MethodsX · 2025Article
- Non-proliferative diabetic retinopathy detection using Rosmarus Quagga optimized explainable generative meta learning based deep convolutional neural network model.International ophthalmology · 2025Article
- Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks.Scientific reports · 2025Article
- Diabetic retinopathy detection based on mobile maxout network and weber local descriptor feature selection using retinal fundus image.Scientific reports · 2025Article
- Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification.Scientific reports · 2025Article
- Research on recognition of diabetic retinopathy hemorrhage lesions based on fine tuning of segment anything model.Scientific reports · 2025Article
- A labeled medical records corpus for the timely detection of rare diseases using machine learning approaches.Scientific reports · 2025Article
- A feature explainability-based deep learning technique for diabetic foot ulcer identification.Scientific reports · 2025Article
- Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model.Scientific reports · 2025Article
- Reducing M2 macrophage in lung fibrosis by controlling anti-M1 agent.Scientific reports · 2025Article
- A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.Scientific reports · 2025Article
- Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data.Scientific reports · 2025Article
- An efficient smart phone application for wheat crop diseases detection using advanced machine learning.PloS one · 2025Article
- Article
Corrections and comments
- Retraction · 2025-09-22Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · Original Data and/or Images not Provided and/or not Available ·
- Retracted
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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