Comparison of deep learning approaches for OCT diagnostics of age-related macular degeneration

Ekaterina A. Lopukhova; Rada R. Ibragimova; Vladislav G. Gruzdev; Ismagil I. Gilmanov; Ruslan V. Kutluyarov; Timur R. Mukhamadeev

doi:10.1117/12.2680764

4 October 2023 Comparison of deep learning approaches for OCT diagnostics of age-related macular degeneration

Ekaterina A. Lopukhova, Rada R. Ibragimova, Vladislav G. Gruzdev, Ismagil I. Gilmanov, Ruslan V. Kutluyarov, Timur R. Mukhamadeev

Author Affiliations +

Proceedings Volume 12743, Optical Technologies for Telecommunications 2022; 127430W (2023) https://doi.org/10.1117/12.2680764
Event: Optical Technologies for Telecommunications 2022, 2022, Ufa, Russian Federation

Abstract

Intelligent automatic diagnosis of diseases, which acts as a recommendation system for diagnosticians, reduces the burden on medical personnel and potentially reduces the impact of the human factor. This circumstance is especially true against the background of rejuvenation of retinal diseases, in particular age-related macular degeneration. In this article, we analysed how neural network models and dataset dimensionality affect the effectiveness of an intelligent diagnostic algorithm for three stages of age-related macular degeneration based on optical coherence tomography images. We identified the advantages and disadvantages of convolutional and recurrent neural networks when dealing with each stage of the disease, as well as different dataset sizes. Based on the obtained information, we concluded the most effective neural network architecture in terms of achievable specificity and sensitivity values, as well as the impact of augmentation for increasing the original dataset on the overall generalization ability of the algorithm.

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Ekaterina A. Lopukhova, Rada R. Ibragimova, Vladislav G. Gruzdev, Ismagil I. Gilmanov, Ruslan V. Kutluyarov, and Timur R. Mukhamadeev "Comparison of deep learning approaches for OCT diagnostics of age-related macular degeneration", Proc. SPIE 12743, Optical Technologies for Telecommunications 2022, 127430W (4 October 2023); https://doi.org/10.1117/12.2680764

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KEYWORDS

Neural networks

Optical coherence tomography

Deep learning

Convolutional neural networks

Diagnostics

Visualization

Retinal diseases

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