Abstract
In this study, a custom Vision Transformer is used for classifying abnormal fundus images and differentiating them from normal ones. The abnormality in images might be due to any of the following six ocular diseases: age-related macular degeneration, cataracts, diabetes, glaucoma, hypertension, and myopia. Three different Vision Transformer architectures with 8, 14, and 24 layers have been used for the classification problem to identify the optimum one. The entire dataset is classified into seven different labels—healthy and six different diseases. The proposed implementation improves on the existing F1-score, precision, sensitivity, and Kappa scores of ocular disease identification presenting a maximum F1-score of 83.49% with 84% sensitivity, 83% precision, and 0.802 Kappa score using Vision Transformer-14.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Flieger J, Dolar-Szczasny J, Rejdak R, Majerek D, Tatarczak-Michalewska M, Proch J, Blicharska E, Flieger W, Baj J, Niedzielski P (2021) The multi-elemental composition of the aqueous humor of patients undergoing cataract surgery, suffering from coexisting diabetes, hypertension, or diabetic retinopathy. Int J Mol Sci 22(17):9413
Hanna K, Rowe F (2017) Health inequalities associated with post-stroke visual impairment in the United Kingdom and Ireland: a systematic review. Neuro-Ophthalmology 41(3):117–136
Abbas Q, Qureshi I, Yan J, Shaheed K (2022) Machine learning methods for diagnosis of eye-related diseases: a systematic review study based on ophthalmic imaging modalities. Arch Comput Methods Eng 29:1–58
Khan IA, Sajeeb A, Fattah SA (2020) An automatic ocular disease detection scheme from enhanced fundus images based on ensembling deep CNN networks. In: 2020 11th International Conference on Electrical and Computer Engineering (ICECE). IEEE, pp 491–494
Chen R, Zeng W, Fan W, Lai F, Chen Y, Lin X, Tang L, Ouyang W, Liu Z, Luop X (2021) Automatic recognition of ocular surface diseases on smartphone images using densely connected convolutional networks. In: 2021 43rd annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 2786–2789
Li C, Ye J, He J, Wang S, Qiao Y, Gu L (2020) Dense correlation network for automated multi-label ocular disease detection with paired color fundus photographs. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE, pp 1–4
Bustamante MG, Carrizosa NM, Ramón SN, Gonzólez PR (2021) Ocular fundus image classification for retinopathology diagnosis. In: 2021 IEEE 2nd international congress of biomedical engineering and bioengineering (CI-IB&BI). IEEE, pp 1–4
He J, Li C, Ye J, Wang S, Qiao Y, Gu L (2020) Classification of ocular diseases employing attention-based unilateral and bilateral feature weighting and fusion. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE, pp 1258–1261
Guergueb T, Akhloufi MA (2021) Ocular diseases detection using recent deep learning techniques. In: 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC). IEEE, pp 3336–3339
Wang Z, Lin L, Wu J, Tang X (2021) Multi-task learning based ocular disease discrimination and faz segmentation utilizing octa images. In: 2021 43rd annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 2790–2793
Nair V, Suranglikar S, Deshmukh S, Gavhane Y (2021) Multi-labelled ocular disease diagnosis enforcing transfer learning. In: 2021 55th annual conference on information sciences and systems (CISS). IEEE, pp 1–6
Cohen JP (2019) Ocular disease intelligent recognition odir-5k. https://odir2019.grand-challenge.org/
Amine K, Redouane K, Bilel M (2022) A redundant wavelet based medical image watermarking scheme for secure transmission in telemedicine applications. Multi-media Tools Appl 82:1–15
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16 Ă— 16 words: transformers for image recognition at scale. In: International conference on learning representations. https://openreview.net/forum?id=YicbFdNTTy
Radhakrishnan P (2021) Why transformers are slowly replacing CNNS in computer vision? https://medium.com/becoming-human/transformers-in-vision-e2e87b739feb
Guo MH, Liu ZN, Mu TJ, Hu SM (2021) Beyond self-attention: external attention using two linear layers for visual tasks. Preprint at arXiv:2105.02358
Huang L, Zhao L, Zhou Y, Zhu F, Liu L, Shao L (2020) An investigation into the stochasticity of batch whitening. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. IEEE, pp 6439–6448
Gummadi SD, Ghosh A (2022) Deep residual learning based discriminator for identifying deepfakes with cut-out regularization. In: 2022 IEEE world conference on applied intelligence and computing (AIC), pp 149–155
Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186. https://aclanthology.org/N19-1423
Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. In: International conference on learning representations. https://openreview.net/forum?id=Bkg6RiCqY7
Gummadi SD, Ghosh A (2021) A transfer learning based approach for detecting covid-19 with radiography images. In: 2021 12th international conference on computing communication and networking technologies (ICCCNT). IEEE, pp 1–5
L3Harris: Calculate confusion matrices. https://www.l3harrisgeospatial.com/docs/calculatingconfusionmatrices.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gummadi, S.D., Ghosh, A. (2023). Classification of Ocular Diseases: A Vision Transformer-Based Approach. In: Roy, S., Sinwar, D., Dey, N., Perumal, T., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision. ICICV 2022. Lecture Notes in Networks and Systems, vol 680. Springer, Singapore. https://doi.org/10.1007/978-981-99-2602-2_25
Download citation
DOI: https://doi.org/10.1007/978-981-99-2602-2_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2601-5
Online ISBN: 978-981-99-2602-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)