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DC poleHodnotaJazyk
dc.contributor.authorQadri, Juwaria
dc.contributor.authorJothi, J. Angel Arul
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-29T18:38:18Z-
dc.date.available2024-07-29T18:38:18Z-
dc.date.issued2024
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 273-282.en
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57399
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjecthistopatologiecs
dc.subjectrakovina tlustého střevacs
dc.subjectstohovací souborcs
dc.subjectvysvětlitelná umělá inteligencecs
dc.subjectGrad-CAMcs
dc.subjectSHAPcs
dc.titleECNNXAI: Ensembled CNNs with eXplainable Artificial Intelligence for Colon Histopathology Image Classificationen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedColon cancer is ranked as the third most commonly diagnosed cancer and second for causing the most cancer related deaths. Histopathology is a crucial diagnostic tool for cancer since it enables the microscopic analysis of tissue samples to pinpoint abnormal cells, to identify the stage of the cancer and its kind. There is a significant need for precise detection and diagnosis from histopathology images. This research proposes a stacking ensemble model called Ensembled Convolutional Neural Networks with eXplainable Artificial Intelligence (ECNNXAI) for mul ticlass colon histopathology image classification. Our ensemble model consists of three pre-trained convolutional neural networks (XceptionNet, DenseNet-121 and InceptionNetV3) as base classifiers and the logistic regression as the meta classifier. Gradient-weighted Class Activation Mapping (Grad-CAM) visualization technique is used to interpret and understand the regions focused by the base classifiers to arrive at the final predictions. SHapley Additive exPlanations (SHAP) is used for understanding the predictions made by the ECNNXAI. The proposed model achieves the best overall performance with accuracy of 72.83%, precision of 77.78%, recall of 66.52% and F1 score of 71.71% on the Chaoyang dataset.en
dc.subject.translatedhistopathologyen
dc.subject.translatedcolon canceren
dc.subject.translatedstacking ensembleen
dc.subject.translatedexplainable artificial intelligenceen
dc.subject.translatedGrad-CAMen
dc.subject.translatedSHAPen
dc.identifier.doihttps://doi.org/10.24132/10.24132/CSRN.3401.28
dc.type.statusPeer revieweden
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