ArticlePhysical and engineering sciences in medicine2022
Retinal fundus image classification for diabetic retinopathy using SVM predictions.
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 9 papers.
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Who cites it
9 citing papers in PubMed, 34 citations in OpenAlex.
- Multi-task deep learning framework combining CNN: vision transformers and PSO for accurate diabetic retinopathy diagnosis and lesion localization.Scientific reports · 2025Article
- Diagnostics of diabetic retinopathy based on fundus photos using machine learning methods with advanced feature engineering algorithms.Scientific reports · 2025Article
- MAFNet: A novel adaptive multi-scale model for fine-grained grading of diabetic retinopathy.Scientific reports · 2025Article
- Pan-Ret: a semi-supervised framework for scalable detection of pan-retinal diseases.Medical & biological engineering & computing · 2025Review
- Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- External Validation of Deep Learning Models for Classifying Etiology of Retinal Hemorrhage Using Diverse Fundus Photography Datasets.Bioengineering (Basel, Switzerland) · 2024Article
- An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates.Scientific reports · 2024Article
- Advances in artificial intelligence models and algorithms in the field of optometry.Frontiers in cell and developmental biology · 2023Review
- Deep attentive convolutional neural network for automatic grading of imbalanced diabetic retinopathy in retinal fundus images.Biomedical optics express · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic Retinopathy (DR) is one of the leading causes of blindness in all age groups. Inadequate blood supply to the retina, retinal vascular exudation, and intraocular hemorrhage cause DR. Despite recent advances in the diagnosis and treatment of DR, this complication remains a challenging task for physicians and patients. Hence, a comprehensive and automated technique for DR screening is necessary, which will give early detection of this disease. The proposed work focuses on 16 class classification method using Support Vector Machine (SVM) that predict abnormalities individually or in combination based on the selected class. Our proposed work comprises Gaussian mixture model (GMM), K-means, Maximum a Posteriori (MAP) algorithm, Principal Component Analysis (PCA), Grey level co-occurrence matrix (GLCM), and SVM for disease diagnosis using DR. The proposed method provides an accuracy of 77.3% on DIARETDB1 dataset. We expect this low computational cost will be helpful in the medicine and diagnosis of DR.
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