ArticleDiagnostics (Basel, Switzerland)2023
Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 52 citations in OpenAlex.
- Artificial intelligence-based quantitative analysis of interocular retinal vascular differences in school-age children with mild to moderate anisometropia.Frontiers in medicine · 2026Article
- Article
- SSLVNet: an explainable multi-modal hybrid framework integrating self-supervised learning and vision transformers for multi-label classification of retinal diseases.Frontiers in artificial intelligence · 2026Article
- VG-RETFound: a hybrid retinal AI framework for early vision impairment screening through vessel-graph learning and cross-modal fusion.Frontiers in medicine · 2026Article
- Non-Invasive Retinal Pathology Assessment Using Haralick-Based Vascular Texture and Global Fundus Color Distribution Analysis.Journal of imaging · 2025Article
- Fundus Image-Based Eye Disease Detection Using EfficientNetB3 Architecture.Journal of imaging · 2025Article
- Pan-Ret: a semi-supervised framework for scalable detection of pan-retinal diseases.Medical & biological engineering & computing · 2025Review
- Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches.Scientific reports · 2025Article
- Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.Scientific reports · 2024Article
- Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence.BMJ open ophthalmology · 2024Article
- Hybrid Models for Endoscopy Image Analysis for Early Detection of Gastrointestinal Diseases Based on Fused Features.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%.
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