Abstract
With the rapid development of digital image processing and machine learning technology, computer-aided diagnosis for ocular diseases is more active in the medical image processing and analysis field. Optical coherence tomography (OCT), as one of the most promising new tomography techniques, has been widely used in the clinical diagnosis of ophthalmology and dentistry. To overcome the lack of professional ophthalmologists and realize the intelligent diagnosis of different ocular diseases, we propose a convolutional neural network (CNN) based on structure feature and visual attention for ocular diseases classification. We firstly preprocess the OCT images according to the OCT data characteristics to enhance the OCT image quality. Meanwhile, we propose to use the CNN with structure prior to classify five kinds of ocular diseases, including age-related macular degeneration (AMD), diabetic macular edema (DME), normal (NM), polypoidal choroidal vasculopathy (PCV), and pathologic myopia (PM). Besides, the visual attention mechanism is also used to enhance the ability of the network to represent effective features. The experimental results show that our method can outperform most of the state-of-the-art algorithms in the classification accuracy of different ocular diseases on the OCT dataset.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 62077037 and 61872241, in part by Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0102, in part by the Science and Technology Commission of Shanghai Municipality under Grants 18410750700 and 17411952600, in part by Shanghai Lin-Gang Area Smart Manufacturing Special Project under Grant ZN2018020202-3, and in part by Project of Shanghai Municipal Health Commission (2018ZHYL0230).
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Wen, Y. et al. (2021). A Classification Network for Ocular Diseases Based on Structure Feature and Visual Attention. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_28
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