Diabetic Retinopathy Stage Classification Using Deep Learning Techniques
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Abstract
This research presents the development of a classifier for detecting the stages of Diabetic Retinopathy (DR) using Convolutional Neural Networks (CNNs), a deep learning–based classification technique. Five architectures, including DenseNet121, EfficientNetB0, EfficientNetB1, MobileNetV2, and NASNetMobile, were explored. A dataset of 5,000 retinal fundus images obtained from Kaggle was categorized into five stages of Diabetic Retinopathy. The research methodology consisted of two main steps: 1) data preparation and 2) model generation. Each CNN architecture was trained for 100 epochs. The experimental results indicated that EfficientNetB0 achieved the highest performance, reaching an accuracy of 99.25% on the training dataset. When evaluated on the test dataset, the model achieved an accuracy of 81.30% and a macro-average F1-score of 81.15%. Furthermore, the model demonstrated exceptional effectiveness in identifying high-risk stages, achieving a precision of 92.82% for the most severe stage of Diabetic Retinopathy and a recall of 89.00% for the severe stage. These findings suggest that the developed classifier has significant potential to support medical screening processes and effectively reduce the risk of vision loss in diabetic patients.
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