Precision Diagnosis of Diabetic Retinopathy Using Exudate-Focused SVM Models
DOI:
https://doi.org/10.57159/jcmm.4.1.25148Keywords:
Diabetic Retinopathy, Digital Image Processing, Machine Learning, Support Vector MachinesAbstract
The global concern over diabetes-related eye diseases continues to grow significantly. Diabetic retinopathy, caused by heightened glucose levels in retinal capillaries, leads to vision clouding and eventual blindness. Early detection through regular screening enables intervention with medication, preventing further vision deterioration. Therefore, this study introduces a smart application that utilizes digital retinal image processing to aid in the prompt identification of diabetic retinopathy. The application streamlines the analysis of eye images, with the goal of automatically classifying the severity of diabetic retinopathy. Through initial image processing, specific features such as blood vessels, microaneurysms, and hard exudates are identified and extracted for classification using a support vector machine (SVM). Evaluation performed on a dataset of 400 retinal images graded on a 4-grade scale of non-proliferative diabetic retinopathy achieved a maximum sensitivity rate of 95%. This application holds significant potential for enabling timely intervention in the treatment of diabetic retinopathy by healthcare professionals. Additionally, the AI-driven approach proposed in this study empowers patients to easily access support services, while providing physicians and researchers with advanced tools for analyzing and predicting diabetic retinopathy data. The resulting reports play a crucial role in assessing the severity of the disease in affected individuals.
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Accepted 2025-01-26
Published 2025-01-27
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