ArticleComputational and mathematical methods in medicine2021
An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Detection of Diabetic Retinopathy Using Discrete Wavelet-Based Center-Symmetric Local Binary Pattern and Statistical Features.Journal of imaging informatics in medicine · 2025Article
- A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification.Scientific reports · 2025Article
- Hybrid adaptive deep learning classifier for early detection of diabetic retinopathy using optimal feature extraction and classification.Journal of diabetes and metabolic disorders · 2023Article
- Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends.Journal of healthcare engineering · 2023Review
- Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.International journal of ophthalmology · 2023Article
- Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2022Review
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Abstract
Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur. It is estimated that one third of more than half a million diabetic patients will have diabetic retinopathy by the 22nd century. Many effective methods have been proposed for disease detection with deep learning. In this study, unlike other studies, a deep learning-based method has been proposed in which diabetic retinopathy lesions are detected automatically and independently of datasets, and the detected lesions are classified. In the first stage of the proposed method, a data pool is created by collecting diabetic retinopathy data from different datasets. With Faster RCNN, lesions are detected, and the region of interests are marked. The images obtained in the second stage are classified using the transfer learning and attention mechanism. The method tested in Kaggle and MESSIDOR datasets reached 99.1% and 100% ACC and 99.9% and 100% AUC, respectively. When the obtained results are compared with other results in the literature, it is seen that more successful results are obtained.
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