Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures

Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures

Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, requiring early detection for effective treatment. Deep learning models have been widely used for automated DR classification, with Convolutional Neural Networks (CNNs) being the most established approach. Recently, Vision...

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Bibliographic Details
Main Authors: Weijie Zhang, Veronika Belcheva, Tatiana Ermakova
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Computers
Subjects:
diabetic retinopathy
deep learning
convolutional neural networks (CNNs)
vision transformers (ViTs)
hybrid models
medical imaging
Online Access:https://www.mdpi.com/2073-431X/14/5/187
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