Retinal Fundus Multi-Disease Image Classification using Hybrid CNN-Transformer-Ensemble Architectures
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
Our research is motivated by the urgent global issue of a large population
affected by retinal diseases, which are evenly distributed but underserved by
specialized medical expertise, particularly in non-urban areas. Our primary
objective is to bridge this healthcare gap by developing a comprehensive
diagnostic system capable of accurately predicting retinal diseases solely from
fundus images. However, we faced significant challenges due to limited, diverse
datasets and imbalanced class distributions. To overcome these issues, we have
devised innovative strategies. Our research introduces novel approaches,
utilizing hybrid models combining deeper Convolutional Neural Networks (CNNs),
Transformer encoders, and ensemble architectures sequentially and in parallel
to classify retinal fundus images into 20 disease labels. Our overarching goal
is to assess these advanced models' potential in practical applications, with a
strong focus on enhancing retinal disease diagnosis accuracy across a broader
spectrum of conditions. Importantly, our efforts have surpassed baseline model
results, with the C-Tran ensemble model emerging as the leader, achieving a
remarkable model score of 0.9166, surpassing the baseline score of 0.9.
Additionally, experiments with the IEViT model showcased equally promising
outcomes with improved computational efficiency. We've also demonstrated the
effectiveness of dynamic patch extraction and the integration of domain
knowledge in computer vision tasks. In summary, our research strives to
contribute significantly to retinal disease diagnosis, addressing the critical
need for accessible healthcare solutions in underserved regions while aiming
for comprehensive and accurate disease prediction.
