Year: 2026
Type: article
Abstract: Retinal fundus images have the challenge of differentiating the stages of diabetic retinopathy, often causing confusion between proliferative retinopathy to severe retinopathy or no retinopathy to mild retinopathy. Without developing complex technologies, human accuracy differed across image datasets. By using a deep learning CNN as the feature extractor and machine learning classifiers, this study achieved a remarkable accuracy of 91%. The ablation study utilised VGG16, Resnet152V2, and Xception as feature extractors together with Random Forests and K-Nearest Neighbour (KNN) classifiers. The best performance was achieved with Xception as the feature extractor and as the classifier Random Forest.
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7142641438
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