ArticlePhysical and engineering sciences in medicine2022
Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by siamese network.
Article in Physical and engineering sciences in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Knowledge distillation-based lightweight MobileNet model for diabetic retinopathy classification.Scientific reports · 2025Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- Artificial intelligence in retinal image analysis for hypertensive retinopathy diagnosis: a comprehensive review and perspective.Visual computing for industry, biomedicine, and art · 2025Review
- Deep learning applications for diabetic retinopathy and retinopathy of prematurity diseases diagnosis: a systematic review.International journal of ophthalmology · 2025Review
- A Survey on Deep-Learning-Based Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2023Review
- Advances in artificial intelligence models and algorithms in the field of optometry.Frontiers in cell and developmental biology · 2023Review
- Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC).Multimedia tools and applications · 2022Article
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Authors and funding
3 authors.
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Abstract
Diabetic retinopathy (DR) is a progressive vascular complication that affects people who have diabetes. This retinal abnormality can cause irreversible vision loss or permanent blindness; therefore, it is crucial to undergo frequent eye screening for early recognition and treatment. This paper proposes a feature extraction algorithm using discriminative multi-sized patches, based on deep learning convolutional neural network (CNN) for DR grading. This comprehensive algorithm extracts local and global features for efficient decision-making. Each input image is divided into small-sized patches to extract local-level features and then split into clusters or subsets. Hierarchical clustering by Siamese network with pre-trained CNN is proposed in this paper to select clusters with more discriminative patches. The fine-tuned Xception model of CNN is used to extract the global-level features of larger image patches. Local and global features are combined to improve the overall image-wise classification accuracy. The final support vector machine classifier exhibits 96% of classification accuracy with tenfold cross-validation in classifying DR images.
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