Version 1
: Received: 18 November 2022 / Approved: 21 November 2022 / Online: 21 November 2022 (12:14:18 CET)
Version 2
: Received: 5 May 2023 / Approved: 8 May 2023 / Online: 8 May 2023 (14:25:29 CEST)
Wang, M. H., Zhou, J., Tang, Z., Huang, C., Yang, J., Huang, L., ... & Chong, K. K. (2023). Fusion Learning Methods for the Age-Related Macular Degeneration Detection Based on Multiple Sources of Ophthalmic Digital Data.
Wang, M. H., Zhou, J., Tang, Z., Huang, C., Yang, J., Huang, L., ... & Chong, K. K. (2023). Fusion Learning Methods for the Age-Related Macular Degeneration Detection Based on Multiple Sources of Ophthalmic Digital Data.
Wang, M. H., Zhou, J., Tang, Z., Huang, C., Yang, J., Huang, L., ... & Chong, K. K. (2023). Fusion Learning Methods for the Age-Related Macular Degeneration Detection Based on Multiple Sources of Ophthalmic Digital Data.
Wang, M. H., Zhou, J., Tang, Z., Huang, C., Yang, J., Huang, L., ... & Chong, K. K. (2023). Fusion Learning Methods for the Age-Related Macular Degeneration Detection Based on Multiple Sources of Ophthalmic Digital Data.
Abstract
Age-related Macular Degeneration (AMD) is the major cause of elders’ vision loss, early screening and treatment are the most efficient way to reduce the rate of blindness. AI-based methods based on ophthalmic images play great potential for AMD diagnosis. However, low levels of accuracy, robustness, and explainability are challenges for AI approaches applied in clinics. Thus, this study proposed a multi-type of data source fusion method and a multi-model fusion approach for AMD detection. Typical unsupervised (Hierarchical Clustering and K-Means), typical supervised (SVM, VGG-16, and ResNet) methods, and proposed methods (multi-source data fusion-based method and multi-model fusion-based approach) are compared based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular color fundus photography (CFP) and Ultra-Wide field Fundus (UWF) images. Data preprocessing and enhancements of each type of data are discussed. A feature extraction based on unsupervised ML models, feature combination and normalization, and multi-layer perception (MLP) algorithm are involved in the proposed multi-source data fusion-based method. Supervised ML and DL models and a voting mechanism are involved in the multi-model fusion-based approach. Findings show that the proposed methods present a high performance of accuracy and robustness. A real-world UWF database is involved from Shenzhen Aier Hospital. Practical and theoretical contributions are delivered. A reference value for medical diagnosis based on multiple digital images is contributed.
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Mini Han Wang
Commenter's Conflict of Interests: Author