Automatic Severity Classification of Diabetic Retinopathy Using Random Forest
Year: 2025
Type: article
Abstract: Diabetic Retinopathy (DR) is a severe vision-threatening condition caused by high blood glucoselevels. Traditional screening by ophthalmologists can be error-prone and time-consuming, leading toincreased interest in automated diagnostic methods. This paper introduces a deep learning approach thatuses DenseNet169 and a Random Forest model to detect DR severity from a single-Color Fundusphotograph, achieving high accuracy, sensitivity, and specificity. Trained on the Kaggle Asia Pacific TeleOphthalmology Society dataset, the model outperforms current methods and offers an efficient solution forautonomous DR diagnosis.
Author: R.K Vidhya, Dr T.V Chithra
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6931021902
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