MaxGlaViT: A Novel Lightweight Vision Transformer-Based Approach for Early Diagnosis of Glaucoma Stages From Fundus Images

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dc.authorid0000-0003-0562-4931
dc.authorid0000-0002-2433-246X
dc.contributor.authorYurdakul, Mustafa
dc.contributor.authorUyar, Kubra
dc.contributor.authorTasdemir, Sakir
dc.date.accessioned2026-01-24T12:30:49Z
dc.date.available2026-01-24T12:30:49Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractGlaucoma is a prevalent eye disease that often progresses without symptoms and can lead to permanent vision loss if not detected early. The limited number of specialists and overcrowded clinics worldwide make it difficult to detect the disease at an early stage. Deep learning-based computer-aided diagnosis (CAD) systems are a solution to this problem, enabling faster and more accurate diagnosis. In this study, we proposed MaxGlaViT, a novel Vision Transformer model based on MaxViT to diagnose different stages of glaucoma. The architecture of the model is constructed in three steps: (i) the Multi Axis Vision Transformer (MaxViT) structure is scaled in terms of the number of blocks and channels, (ii) low-level feature extraction is improved by integrating the attention mechanism into the stem block, and (iii) high-level feature extraction is improved by using the modern convolutional structure. The MaxGlaViT model was tested on the HDV1 fundus image data set and compared to a total of 80 deep learning models. The results show that the MaxGlaViT model, which contains effective block structures, outperforms previous literature methods in terms of both parameter efficiency and classification accuracy. The model performs particularly high success in detecting the early stages of glaucoma. MaxGlaViT is an effective solution for multistage diagnosis of glaucoma with low computational cost and high accuracy. In this respect, it can be considered as a candidate for a scalable and reliable CAD system applicable in clinical settings.
dc.identifier.doi10.1002/ima.70159
dc.identifier.issn0899-9457
dc.identifier.issn1098-1098
dc.identifier.issue4
dc.identifier.scopus2-s2.0-105011287850
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/ima.70159
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5455
dc.identifier.volume35
dc.identifier.wosWOS:001531211700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal of Imaging Systems and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectConvNeXtV2
dc.subjectECA
dc.subjecteye diseases
dc.subjectglaucoma diagnosis
dc.subjectMaxViT
dc.subjectvision transformer
dc.titleMaxGlaViT: A Novel Lightweight Vision Transformer-Based Approach for Early Diagnosis of Glaucoma Stages From Fundus Images
dc.typeArticle

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