Název:	ECNNXAI: Ensembled CNNs with eXplainable Artificial Intelligence for Colon Histopathology Image Classification
Autoři:	Qadri, Juwaria Jothi, J. Angel Arul
Citace zdrojového dokumentu:	WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 273-282.
Datum vydání:	2024
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/57399
ISSN:	2464–4625 (online) 2464–4617 (print)
Klíčová slova:	histopatologie;rakovina tlustého střeva;stohovací soubor;vysvětlitelná umělá inteligence;Grad-CAM;SHAP
Klíčová slova v dalším jazyce:	histopathology;colon cancer;stacking ensemble;explainable artificial intelligence;Grad-CAM;SHAP
Abstrakt v dalším jazyce:	Colon 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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2024: Full Papers Proceedings

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