A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer

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dc.contributor.authorYusufoglu, Elif
dc.contributor.authorFirat, Huseyin
dc.contributor.authorUzen, Huseyin
dc.contributor.authorOzcelik, Salih Taha Alperen
dc.contributor.authorCicek, Ipek Balikci
dc.contributor.authorSengur, Abdulkadir
dc.contributor.authorAtila, Orhan
dc.date.accessioned2026-04-04T13:31:10Z
dc.date.available2026-04-04T13:31:10Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground/Objectives: Age-related macular degeneration (AMD) is a significant cause of vision loss in older adults, often progressing without early noticeable symptoms. Deep learning (DL) models, particularly convolutional neural networks (CNNs), demonstrate potential in accurately diagnosing and classifying AMD using medical imaging technologies like optical coherence to-mography (OCT) scans. This study introduces a novel CNN-based DL method for AMD diagnosis, aiming to enhance computational efficiency and classification accuracy. Methods: The proposed method (PM) combines modified Inception modules, Depthwise Squeeze-and-Excitation Blocks, and ConvMixer architecture. Its effectiveness was evaluated on two datasets: a private dataset with 2316 images and the public Noor dataset. Key performance metrics, including accuracy, precision, recall, and F1 score, were calculated to assess the method's diagnostic performance. Results: On the private dataset, the PM achieved outstanding performance: 97.98% accuracy, 97.95% precision, 97.77% recall, and 97.86% F1 score. When tested on the public Noor dataset, the method reached 100% across all evaluation metrics, outperforming existing DL approaches. Conclusions: These results highlight the promising role of AI-based systems in AMD diagnosis, of-fering advanced feature extraction capabilities that can potentially enable early detection and in-tervention, ultimately improving patient care and outcomes. While the proposed model demon-strates promising performance on the datasets tested, the study is limited by the size and diversity of the datasets. Future work will focus on external clinical validation to address these limita-tions.
dc.description.sponsorshipFimath;rat University, Scientific Research Project Committee
dc.description.sponsorshipThis study was supported by F & imath;rat University, Scientific Research Project Committee, under grant No. TEKF.24.47.
dc.identifier.doi10.3390/diagnostics14242836
dc.identifier.issn2075-4418
dc.identifier.issue24
dc.identifier.orcid0000-0003-1202-6841
dc.identifier.orcid0000-0003-1614-2639
dc.identifier.orcid0000-0002-1257-8518
dc.identifier.orcid0000-0002-7929-7542
dc.identifier.orcid0000-0001-7211-913X
dc.identifier.orcid0000-0002-3805-9214
dc.identifier.orcid0000-0002-0998-2130
dc.identifier.pmid39767197
dc.identifier.scopus2-s2.0-85213304128
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics14242836
dc.identifier.urihttps://hdl.handle.net/11616/108621
dc.identifier.volume14
dc.identifier.wosWOS:001384941800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectConvMixer
dc.subjectdepthwise squeeze-and-excitation block
dc.subjectmodified inception module
dc.subjectage-related macular degeneration
dc.subjectoptical coherence tomography
dc.titleA Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer
dc.typeArticle

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