A framework for robust glaucoma detection: A confidence-aware deep uncertainty quantification approach with a comprehensive assessment for enhanced clinical decision-making

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dc.authorscopusidReza Tavakkoli-Moghaddam / 57207533714
dc.authorwosidReza Tavakkoli-Moghaddam / P-1948-2015
dc.contributor.authorZarean, Javad
dc.contributor.authorTajally, AmirReza
dc.contributor.authorTavakkoli-Moghaddam, Reza
dc.contributor.authorSajadi, Seyed Mojtaba
dc.contributor.authorWassan, Niaz
dc.date.accessioned2025-04-17T11:45:01Z
dc.date.available2025-04-17T11:45:01Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractGlaucoma poses a significant threat to public health worldwide, as it can result in irreversible vision loss. Timely identification is vital for halting the progression of visual field deterioration. In recent years, deep neural networks (DNNs) have become increasingly popular in medical imaging due to their ability to identify patterns. As a result, this study introduces a new computer-aided diagnosis (CAD) system based on deep learning (DL) algorithms for glaucoma detection that extracts meaningful features from retinal fundus images (RFIs) and employs uncertainty quantification (UQ) models, including Monte Carlo dropout (MCD), ensemble Bayesian, and ensemble Monte Carlo dropout (EMCD), to generate both point estimates and confidence values for the outputs, thereby capturing the uncertainty associated with the classifications. The proposed framework is validated using well-known clinical datasets, and the reliability of the outputs is evaluated using comprehensive performance metrics such as expected calibration error (ECE), entropy analysis, and a multi-criteria UQ assessment. Experimental results demonstrate the superiority of the ensemble model, with uncertainty accuracies registering at 97.64%, 97.26%, and 98.97% for the "ACRIMA", "RIM-ONE-DL", and "ORIGA" datasets, respectively. Moreover, the proposed algorithms can alert users to the majority of erroneous diagnoses by assigning uncertainty labels, providing valuable insights for clinicians in glaucoma detection. Such tools can assist healthcare professionals in reducing the probability of misdiagnosis and ensuring that patients receive timely and appropriate treatment.
dc.identifier.citationZarean, J., Tajally, A., Tavakkoli-Moghaddam, R., Sajadi, S. M., & Wassan, N. (2025). A framework for robust glaucoma detection: A confidence-aware deep uncertainty quantification approach with a comprehensive assessment for enhanced clinical decision-making. Engineering Applications of Artificial Intelligence, 139, 109651.
dc.identifier.doi10.1016/j.engappai.2024.109651
dc.identifier.endpage16
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85208677874
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.engappai.2024.109651
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6230
dc.identifier.volume139
dc.identifier.wosWOS:001357550700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTavakkoli-Moghaddam, Reza
dc.institutionauthoridReza Tavakkoli-Moghaddam / 0000-0002-6757-926X
dc.language.isoen
dc.publisherElsevier ltd
dc.relation.ispartofEngineering applications of artificial intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDeep Learning
dc.subjectExpected Calibration Error
dc.subjectGlaucoma Detection
dc.subjectTransfer Learning
dc.subjectUncertainty Quantification
dc.titleA framework for robust glaucoma detection: A confidence-aware deep uncertainty quantification approach with a comprehensive assessment for enhanced clinical decision-making
dc.typeArticle

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