Fundus Disease Image Classification based on Improved Transformer
Abstract
In recent years, the progress of deep learning and fundus camera technology makes it possible to diagnose fundus diseases by computer. However, the fundus image dataset is relatively small, which makes the pure transformer model challenging to be applied to medical disease analysis. Therefore, this paper proposes a Transformer Eye (TransEye) fine-grained fundus disease image classification method based on the self-attention mechanism to assist diagnosis. TransEye combines the advantages of Convolution Neural Network (CNN) and Transformer model. It can not only effectively extract the underlying features, but also establish the remote dependence of the image. So, it can locate the most discriminative image area and complete end-to-end training. Evaluated the classification effect of our method on the preprocessed OIA dataset, the experimental results show the superiority of TransEye.
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Most works in medical image classification jointly adopt CNN and transformer but did not develop a transformer-based backbone without convolutions to capture multi-granularity information in medical images.
“Most works [30 -34] in medical image classi fi cation jointly adopt the CNN and transformer model but did not develop a transformer-based backbone without convolutions to capture multi-granularity information in medical images.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [30]
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- Authors
- Yang, H., Chen, J., & Xu, M.
- Affiliations
- Hubei Normal University (CN)
- Venue
- 2021 International Conference on Neuromorphic Computing (ICNC)
- Publisher
- IEEE
- Published
- 2021-10-15
- Pages
- 207-214
- OpenAlex
- W3216097109
- Cited by
- 31
- References
- 28
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 4 · 2023: 10 · 2022: 6
- FWCI
- 6.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Neuromorphic Computing (ICNC) (publishedVersion)
- Funders
- Natural Science Foundation of China (61976085)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- fundus images, transformer models, self-attention, CNN-Transformer hybrid, small medical image datasets, eye disease diagnosis
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