Scholarlink

This study explores a comparative analysis of base and ensemble classifiers for detecting breast cancer. The classifiers analyze data from fine needle aspiration biopsies to categorize samples as benign or malignant. Artificial intelligence (AI), specifically eXplainable AI (XAI), plays a vital role in this study by providing interpretability to the classification results. Initially, classifier models are built and optimized to ensure accurate predictions. Their performance is then evaluated using metrics like F1 score and recall to identify the most effective model. The best-performing classifier is further analyzed using SHapley Additive exPlanations (SHAP), an XAI method that highlights the significance of each feature and its contribution to the model's decisions. This approach enhances understanding of the classifier's reasoning, which is crucial for medical applications. Finally, a web application is developed to present classifier performance metrics and illustrate how decisions were made, improving transparency and usability.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluating and Interpreting Classifiers for Breast Cancer Detection Using eXplainable AI

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Dedeepya Sai Gondi,
  • Vamsi Krishna Reddy Bandaru,
  • Hemanth Volikatla,
  • Veera Venkata Raghunath Indugu,
  • Srinivas Reddy Bandaru

摘要

This study explores a comparative analysis of base and ensemble classifiers for detecting breast cancer. The classifiers analyze data from fine needle aspiration biopsies to categorize samples as benign or malignant. Artificial intelligence (AI), specifically eXplainable AI (XAI), plays a vital role in this study by providing interpretability to the classification results. Initially, classifier models are built and optimized to ensure accurate predictions. Their performance is then evaluated using metrics like F1 score and recall to identify the most effective model. The best-performing classifier is further analyzed using SHapley Additive exPlanations (SHAP), an XAI method that highlights the significance of each feature and its contribution to the model's decisions. This approach enhances understanding of the classifier's reasoning, which is crucial for medical applications. Finally, a web application is developed to present classifier performance metrics and illustrate how decisions were made, improving transparency and usability.