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This research introduces a cutting-edge artificial neural network (ANN) methodology for differentiating Binary Classification of Breast Cancer into categories Malignant and Benign. Our designed ANN structure accomplishes a remarkable accuracy that has setback the traditional machine learning and ensemble/hybrid models, by implementing advanced deep learning techniques to learn complex patterns and their relationships with the WBCD dataset related to breast cancer. By means of comprehensive assessment, we showed the dependability and flexibility of the ANN model; its performance is measured with various metrics, in order to improve the disease classification with a 100% detection rate and preventing false positives and false negatives; our work advances in an accurate decision support tool for medical professionals.

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ANN-Based Binary Classification for Breast Cancer: A Comparative Study with Machine Learning Models

  • 章节

  • 发表日期：

    2025年

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  • Shafiq Ahamed,
  • Amitabh Wahi

摘要

This research introduces a cutting-edge artificial neural network (ANN) methodology for differentiating Binary Classification of Breast Cancer into categories Malignant and Benign. Our designed ANN structure accomplishes a remarkable accuracy that has setback the traditional machine learning and ensemble/hybrid models, by implementing advanced deep learning techniques to learn complex patterns and their relationships with the WBCD dataset related to breast cancer. By means of comprehensive assessment, we showed the dependability and flexibility of the ANN model; its performance is measured with various metrics, in order to improve the disease classification with a 100% detection rate and preventing false positives and false negatives; our work advances in an accurate decision support tool for medical professionals.