Central serous retinopathy classification with deep learning-based multilevel feature extraction from optical coherence tomography images

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dc.contributor.authorÜzen, Hüseyin
dc.contributor.authorFırat, Hüseyin
dc.contributor.authorAlperen Özçelik, Salih Taha
dc.contributor.authorYusufoğlu, Elif
dc.contributor.authorÇiçek, İpek Balıkçı
dc.contributor.authorŞengür, Abdulkadir
dc.date.accessioned2025-02-22T14:10:54Z
dc.date.available2025-02-22T14:10:54Z
dc.date.issued2025
dc.departmentDicle Üniversitesien_US
dc.description.abstractCentral Serous Chorioretinopathy (CSCR) is an ocular disease characterized by fluid accumulation under the retina, which can lead to permanent visual impairment if not diagnosed early. This study presents a deep learning-based Convolutional Neural Network (CNN) model designed to automatically diagnose acute and chronic CSCR from Optical Coherence Tomography (OCT) images through multi-level feature extraction. The proposed CNN architecture consists of consecutive layers like a traditional CNN. However, it also extracts various features by creating feature maps at four different levels (F1, F2, F3, F4) for the final feature map. The model processes information using group-wise convolution and Pointwise Convolution Block (PCB) at each level. In this way, each feature group is further processed to obtain more representative features, enabling more independent learning. After the PCB outputs, the 4 feature maps are vectorized and combined, thus creating the final feature map. Finally, classification prediction scores are obtained by applying a fully connected layer and softmax function to this feature map. The experimental study utilized two datasets obtained from Elazığ Ophthalmology Polyclinic. The dataset includes 3860 OCT images from 488 individuals, with images categorized into acute CSCR, chronic CSCR, wet AMD, dry AMD, and healthy controls. Our proposed method achieves an increase in accuracy of 0.77%, attaining 96.40% compared to the highest previous accuracy of 95.73% by ResNet101. Precision is enhanced by 0.95%, reaching 95.16% over ResNet101′s 94.21%. The sensitivity (recall) is improved by 0.90%, achieving 95.65% versus ResNet101′s 94.75%. Additionally, the F1 score is increased by 0.93%, attaining 95.38% compared to ResNet101′s 94.45%. These results illustrate the effectiveness of our method, offering more precise and reliable diagnostic capabilities in OCT image classification. In conclusion, this study demonstrates the potential of artificial intelligence-supported diagnostic tools in the analysis of OCT images and contributes significantly to the development of early diagnosis and treatment strategies. © 2025 Elsevier Ltden_US
dc.identifier.doi10.1016/j.optlastec.2025.112519
dc.identifier.issn0030-3992
dc.identifier.scopus2-s2.0-85216247313en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.optlastec.2025.112519
dc.identifier.urihttps://hdl.handle.net/11468/29870
dc.identifier.volume184en_US
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofOptics and Laser Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250222
dc.subjectCentral serous chorioretinopathyen_US
dc.subjectDeep learningen_US
dc.subjectFeature integrationen_US
dc.subjectOptical coherence tomographyen_US
dc.titleCentral serous retinopathy classification with deep learning-based multilevel feature extraction from optical coherence tomography imagesen_US
dc.typeArticleen_US

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