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Related Experiment Video

Updated: Aug 20, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers.

Ahmed A Alsheikhy1, Yahia Said1, Tawfeeq Shawly2

  • 1Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|November 26, 2022
PubMed
Summary

This study introduces an automated breast cancer diagnosis system using AlexNet (a Convolutional Neural Network) and classifiers. The system achieves over 98% accuracy, improving breast cancer detection and classification for clinical use.

Keywords:
BCICCNNbiomedical diagnosisbreast cancerfuzzy algorithm

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Breast cancer remains a leading cause of mortality in women.
  • Mammography is a primary tool for breast cancer detection, but current algorithms lack optimal accuracy and efficiency.
  • Accurate and automated breast cancer identification and classification are critical for timely intervention.

Purpose of the Study:

  • To propose a fully automated biomedical diagnosis system for breast cancer detection and classification.
  • To enhance diagnostic accuracy and reduce computational complexity compared to existing methods.
  • To validate the system's performance using diverse datasets and comparative analysis.

Main Methods:

  • Development of an automated system integrating AlexNet (Convolutional Neural Network) with multiple classifiers.
  • Utilization of a neuro-fuzzy method and a segmentation algorithm for feature extraction and classification.
  • Testing the system on three Kaggle datasets, evaluating performance using accuracy, precision, recall, specificity, and F-score.

Main Results:

  • The proposed system demonstrated superior classification results, outperforming existing literature.
  • Achieved an average accuracy exceeding 98.6%, with other performance metrics also surpassing 98%.
  • Quantitative and qualitative evaluations confirmed the system's high efficacy in breast cancer diagnosis.

Conclusions:

  • The developed automated system offers a significant advancement in breast cancer diagnosis accuracy.
  • The approach shows potential as a valuable tool to assist clinicians in making correct breast cancer diagnoses.
  • The high performance metrics suggest clinical applicability for improving patient outcomes.