Classification of Precancerous Colorectal Lesions via ConvNeXt on Histopathological Images

dc.contributor.authorNergiz, Mehmet
dc.date.accessioned2025-03-08T18:25:50Z
dc.date.available2025-03-08T18:25:50Z
dc.date.issued2023
dc.departmentDicle Üniversitesi
dc.descriptionDÜBAP Project No: MÜHENDİSLİK.22.001
dc.description.abstractIn this translational study, the classification of precancerous colorectal lesions is performed by the ConvNeXt method on MHIST histopathological imaging dataset. The ConvNeXt method is the modernized ResNet-50 architecture having some training tricks inspired by Swin Transformers and ResNeXT. The performance of the ConvNeXt models are benchmarked on different scenarios such as ‘full data’, ‘gradually increasing difficulty based data’ and ‘k-shot data’. The ConvNeXt models outperformed almost all the other studies which are applied on MHIST by using ResNet models, vision transformers, weight distillation, self-supervised learning and curriculum learning strategy in terms of different scenarios and metrics. The ConvNeXt model trained with ‘full data’ yields the best result with the score of 0.8890 for accuracy, 0.9391 for AUC, 0.9121 for F1 and 0.7633 for cohen’s cappa.
dc.description.sponsorship[TR] Dicle Üniversitesi
dc.identifier.doi10.17694/bajece.1240284
dc.identifier.endpage137
dc.identifier.issn2147-284X
dc.identifier.issn2147-284X
dc.identifier.issue2
dc.identifier.startpage129
dc.identifier.urihttps://doi.org/10.17694/bajece.1240284
dc.identifier.urihttps://hdl.handle.net/11468/30420
dc.identifier.volume11
dc.language.isoen
dc.publisherBalkan Yayın
dc.relation.ispartofBalkan Journal of Electrical and Computer Engineering
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_21250205
dc.subjectConvNeXt
dc.subjectCNN
dc.subjectVision Transformer
dc.subjectColorectal Cancer
dc.subjectHistopathology
dc.titleClassification of Precancerous Colorectal Lesions via ConvNeXt on Histopathological Images
dc.typeArticle

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