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Fundus-DeepNet: Multi-Label Deep Learning Classification System for Enhanced Detection of Multiple Ocular Diseases through Data Fusion of Fundus Images
Al-Fahdawi, S. Al-Waisy, A.S. Zeebaree, D.Q. Qahwaji, Rami S.R. Natiq, H. Mohammed, M.A. Nedoma, J. Martinek, R. Deveci, M.
Al-Fahdawi, S.
Al-Waisy, A.S.
Zeebaree, D.Q.
Qahwaji, Rami S.R.
Natiq, H.
Mohammed, M.A.
Nedoma, J.
Martinek, R.
Deveci, M.
Publication Date
2024
End of Embargo
Supervisor
Rights
(c) 2024 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/)
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2023-09-27
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Department
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Embargo end date
Additional title
Abstract
Detecting multiple ocular diseases in fundus images is crucial in ophthalmic diagnosis. This study introduces the Fundus-DeepNet system, an automated multi-label deep learning classification system designed to identify multiple ocular diseases by integrating feature representations from pairs of fundus images (e.g., left and right eyes). The study initiates with a comprehensive image pre-processing procedure, including circular border cropping, image resizing, contrast enhancement, noise removal, and data augmentation. Subsequently, discriminative deep feature representations are extracted using multiple deep learning blocks, namely the High-Resolution Network (HRNet) and Attention Block, which serve as feature descriptors. The SENet Block is then applied to further enhance the quality and robustness of feature representations from a pair of fundus images, ultimately consolidating them into a single feature representation. Finally, a sophisticated classification model, known as a Discriminative Restricted Boltzmann Machine (DRBM), is employed. By incorporating a Softmax layer, this DRBM is adept at generating a probability distribution that specifically identifies eight different ocular diseases. Extensive experiments were conducted on the challenging Ophthalmic Image Analysis-Ocular Disease Intelligent Recognition (OIA-ODIR) dataset, comprising diverse fundus images depicting eight different ocular diseases. The Fundus-DeepNet system demonstrated F1-scores, Kappa scores, AUC, and final scores of 88.56%, 88.92%, 99.76%, and 92.41% in the off-site test set, and 89.13%, 88.98%, 99.86%, and 92.66% in the on-site test set.In summary, the Fundus-DeepNet system exhibits outstanding proficiency in accurately detecting multiple ocular diseases, offering a promising solution for early diagnosis and treatment in ophthalmology.
Version
Published version
Citation
Al-Fahdawi S, Al-Waisy AS, Zeebaree DQ et al (2024) Fundus-DeepNet: Multi-Label Deep Learning Classification System for Enhanced Detection of Multiple Ocular Diseases through Data Fusion of Fundus Images. Information Fusion. 102: 102059.
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Article