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dc.contributor.authorAl-Fahdawi, S.
dc.contributor.authorAl-Waisy, A.S.
dc.contributor.authorZeebaree, D.Q.
dc.contributor.authorQahwaji, Rami
dc.contributor.authorNatiq, H.
dc.contributor.authorMohammed, M.A.
dc.contributor.authorNedoma, J.
dc.contributor.authorMartinek, R.
dc.contributor.authorDeveci, M.
dc.date.accessioned2023-09-29T20:53:23Z
dc.date.accessioned2023-10-06T07:45:05Z
dc.date.available2023-09-29T20:53:23Z
dc.date.available2023-10-06T07:45:05Z
dc.date.issued2024
dc.identifier.citationAl-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.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19604
dc.descriptionYesen_US
dc.description.abstractDetecting 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.en_US
dc.description.sponsorshipEuropean Union under the REFRESH – Research Excellence for Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Program Just Transition. The Ministry of Education, Youth, and Sports of the Czech Republic - Technical University of Ostrava, Czechia under Grants SP2023/039 and SP2023/042.en_US
dc.language.isoenen_US
dc.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/)en_US
dc.subjectFundus imagesen_US
dc.subjectDeep learningen_US
dc.subjectData fusionen_US
dc.subjectOIA-ODIR dataseten_US
dc.subjectHigh-resolution networken_US
dc.subjectFeature level fusionen_US
dc.titleFundus-DeepNet: Multi-Label Deep Learning Classification System for Enhanced Detection of Multiple Ocular Diseases through Data Fusion of Fundus Imagesen_US
dc.status.refereedYesen_US
dc.date.Accepted2023-09-27
dc.date.application2023-09-29
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.identifier.doihttps://doi.org/10.1016/j.inffus.2023.102059
dc.rights.licenseCC-BYen_US
dc.date.updated2023-09-29T20:53:25Z
refterms.dateFOA2023-10-06T07:45:33Z
dc.openaccess.statusopenAccessen_US


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