Comparative analysis of CNN, Transformer, and Mamba-based models for automated eye disease classification from fundus images.

Authors

Md Ahanaf Arif Khan, Sangeeta Biswas

Published in

PloS one. Volume 21. Issue 10. Pages e0360171. Epub Oct 26, 2026.

Abstract

We present a comparative analysis of six models belonging to Convolutional Neural Network (CNN), Transformer, and Mamba families for automated eye disease classification from fundus images under identical experimental conditions. To the best of our knowledge, our work is the first to explore Mamba-based models for automated multi-class eye disease classification from color fundus images. Our analysis suggests that, when evaluated fairly, modern CNN, Transformer, and Mamba-based models provide similar performance for eye disease classification on the Eye Disease Image Dataset (EDID). Along with analyzing individual model performance, we further evaluate performance using a logit-level ensemble of the top three models. The ensemble results imply that these model families capture partially complementary visual features, resulting in a slight performance gain when combined. In both cases, class-wise performance examination indicates that diseases with distinctive structural characteristics are recognized more consistently, while categories with subtle or overlapping features remain challenging. Our implementation, including the training and evaluation pipelines as well as the data splits, is publicly available at https://github.com/ahanaf019/EyeDisease2026.

PMID:
42932327
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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