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Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection.

Sadia Safdar1, Muhammad Rizwan1, Thippa Reddy Gadekallu2

  • 1Department of Computer Science, Kinnaird College for Women, Lahore 44000, Pakistan.

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Summary
This summary is machine-generated.

This study introduces a machine learning model for accurate breast cancer detection, achieving 97.7% accuracy with low false positive and negative rates. The computer-aided detection system aids radiologists in identifying cancerous tumors early.

Keywords:
K-nearest neighbor (KNN)breast cancercomputer-aided detection (CAD)deep learningmachine learningsupport vector machine (SVM)

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Oncology

Background:

  • Breast cancer is a leading cause of mortality in women globally.
  • Early and accurate diagnosis is crucial for improving survival rates.
  • Computer-aided detection (CAD) systems assist radiologists in distinguishing between normal and abnormal breast tissue.

Purpose of the Study:

  • To evaluate machine learning techniques for breast cancer recurrence rate estimation.
  • To develop an accurate computer-aided detection system for breast cancer classification.
  • To minimize false positive and false negative rates in breast cancer diagnosis.

Main Methods:

  • Overview of imaging modalities: ultrasound, histography, and mammography.
  • Application of machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbor (KNN).
  • Data preprocessing, including noise reduction and transformation, with a 60/40 train-test split.

Main Results:

  • Achieved a highest accuracy of 97.7% in breast cancer classification.
  • Reported a low false positive rate (FPR) of 0.01 and false negative rate (FNR) of 0.03.
  • Obtained an Area Under the ROC Curve (AUC) score of 0.99, indicating high model performance.

Conclusions:

  • The proposed machine learning model demonstrates high accuracy and effectiveness in classifying breast tumors.
  • The system successfully overcomes limitations of previous research in breast cancer detection.
  • Future research directions include classification and segmentation challenges in breast cancer detection.