Scholarlink

Cancer, a global health problem, claims many lives annually, with breast cancer (BC) being the most prevalent, accounting for 11.7% of the 19.3 million cases reported in GLOBOCAN2020. Early detection of breast tumors is critical yet challenging. Digital X-ray mammography aids in detection but interpreting mammograms can overlook abnormalities due to the volume of images. To improve detection, computer-assisted-diagnosis (CAD) methods have been proposed. This study aims to classify BC using image-based descriptors and Machine Learning (ML) Hard Voting Ensemble technique, combining algorithms like Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). The process involves preprocessing, feature selection using the Boruta algorithm, and training RF, LR, and SVM. A Hard Voting Ensemble model is then created from their predictions. Validation includes five metrics and statistical analysis, including Receiver Operating Characteristic Curve (ROC) analysis for the Area Under the Curve (AUC). The SVM model performed best with AUC = 0.9647 and an accuracy (ACC) equals to 0.9286, followed by RF with AUC = 0.9624 and LR with AUC = 0.9553. The ensemble model achieved AUC = 0.9471 and ACC = 0.9286.

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

Breast Tumor Classification Using Mammography Image Descriptors as an Input Source: A Machine Learning Hard Voting Ensemble Approach

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Eduardo de Avila-Armenta,
  • Gemma E. Gutierrez-Banuelos,
  • Jose M. Celaya-Padilla,
  • Carlos E. Galvan-Tejada,
  • Manuel A. Soto-Murillo,
  • Antonio Martinez-Torteya,
  • Jorge I. Galvan-Tejada,
  • Jose J. Alvarado-Padilla

摘要

Cancer, a global health problem, claims many lives annually, with breast cancer (BC) being the most prevalent, accounting for 11.7% of the 19.3 million cases reported in GLOBOCAN2020. Early detection of breast tumors is critical yet challenging. Digital X-ray mammography aids in detection but interpreting mammograms can overlook abnormalities due to the volume of images. To improve detection, computer-assisted-diagnosis (CAD) methods have been proposed. This study aims to classify BC using image-based descriptors and Machine Learning (ML) Hard Voting Ensemble technique, combining algorithms like Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). The process involves preprocessing, feature selection using the Boruta algorithm, and training RF, LR, and SVM. A Hard Voting Ensemble model is then created from their predictions. Validation includes five metrics and statistical analysis, including Receiver Operating Characteristic Curve (ROC) analysis for the Area Under the Curve (AUC). The SVM model performed best with AUC = 0.9647 and an accuracy (ACC) equals to 0.9286, followed by RF with AUC = 0.9624 and LR with AUC = 0.9553. The ensemble model achieved AUC = 0.9471 and ACC = 0.9286.