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Retinal multi-disease classification using the varices feature-based dual-channel network

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Abstract

Fundus disease is the main cause of visual defect in the cases of non-congenital visual disability, where diabetic retinopathy, ischemic optic neuropathy, optic neuritis, and glaucoma are the most common diseases. Early detection and treatment are the key to control fundus lesions. At present, manual diagnosis may lead to the problem of wasting time and misdiagnosis. On this basis, this paper proposes a dual-channel network for multi-disease diagnosis based on retinal varices features and presents a complete fundus retinal image-assisted diagnosis solution. Firstly, on the advice of ophthalmologists, the retinal varices features of various diseases are extracted. Then, combined with the varices attention mechanism, a dual-channel network retinal multi-disease classification model (VAM-DCN) is constructed. Finally, the retinal varices features are put into a dual channel for network learning and training. The proposed method is verified on the clinical data (normal retina, diabetic retinopathy, ischemic optic neuropathy, optic neuritis, and glaucoma) of Dalian NO.3 People's Hospital, and the precision, recall, F1-score, and accuracy can reach 99.44%, 99.39%, 99.41%, and 99.4%, respectively. The proposed method can help ophthalmologist realize the multi-disease classification of fundus retinal images, reduce the possibility of misdiagnosis and missed diagnosis, which has certain clinical medical value.

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Data availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by Natural Science Foundation of Liaoning Province under Grant 2021-MS-272, and Educational Committee project of Liaoning Province under Grant LJKQZ2021088. Thanks to Siyu Sun, ophthalmologist of Dalian NO.3 People's Hospital, for providing clinical data for this paper.

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  1. School of Computer Science and Artificial Intelligence, Liaoning Normal University, Dalian City, Liaoning Province, China

    Lingling Fang & Huan Qiao

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  1. Lingling Fang
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  2. Huan Qiao
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Correspondence to Lingling Fang.

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Fang, L., Qiao, H. Retinal multi-disease classification using the varices feature-based dual-channel network. Multimed Tools Appl 83, 42629–42644 (2024). https://doi.org/10.1007/s11042-023-17127-6

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  • DOI: https://doi.org/10.1007/s11042-023-17127-6

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