Explainable extreme boosting model for breast cancer diagnosis

Abstract

<span lang="EN-US">This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%.</span>

Integration of feature enhancement technique in Google inception network for breast cancer detection and classification

“…After LLP feature enhancement techniques are applied the LPP-enhanced features are concatenated with the original features obtained from the base network which aims to preserve both the enhanced discriminative information and the original representation captured by the base network and the concatenation operation combines the feature maps of the LPP-enhanced features and the original features, resulting in a combined feature representation. Following the feature concatenation, the modified network integrates a classifier to perform breast cancer detection and classification [43][44][45][46][47][48][49][50][51][52]. In this paper, we use SoftMax classifiers and trained on the combined features, enabling it to learn the discriminative patterns and make accurate predictions.…”