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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers.

Dina A Ragab1,2, Maha Sharkas1, Omneya Attallah1

  • 1Electronics and Communications Engineering Department, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria 1029, Egypt.

Diagnostics (Basel, Switzerland)
|November 14, 2019
PubMed
Summary
This summary is machine-generated.

A new computer-aided detection (CAD) system accurately identifies breast cancer lesions in mammograms. This system achieved 100% accuracy after feature selection, improving early detection capabilities.

Keywords:
feature selectionthe computer-aided detectionthe decision treesthe k-nearest neighborthe pectoral muscle removalthe statistical features

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer remains a significant global health concern, necessitating advanced diagnostic tools.
  • Mammography is a primary screening method, but interpretation can be challenging.
  • Computer-aided detection (CAD) systems offer potential to enhance diagnostic accuracy.

Purpose of the Study:

  • To introduce and evaluate a novel computer-aided detection (CAD) system for breast cancer lesion classification.
  • To assess the impact of image enhancement, feature extraction, and feature selection on CAD system performance.
  • To compare the efficacy of k-nearest neighbor (k-NN) and decision tree classifiers, including multiple classifier systems (MCS).

Main Methods:

  • Mammogram images underwent contrast enhancement, pectoral muscle elimination, and breast suppression.
  • Statistical features were extracted from the processed mammograms.
  • k-nearest neighbor (k-NN) and decision tree classifiers were employed, alongside cascaded and parallel multiple classifier systems (MCS).
  • Two wrapper feature selection (FS) approaches were utilized to identify influential features.
  • The system was validated using combined datasets from the MIAS database and the digital mammography dream challenge.

Main Results:

  • The proposed CAD system achieved a maximum accuracy of 99.7% before feature selection using Adaboosting with J48 decision tree classifiers.
  • Following feature selection, the k-NN classifier achieved a perfect accuracy of 100%.
  • The area under the receiver operating characteristic (ROC) curve (AUC) reached 1.0, indicating excellent discriminatory performance.
  • The system demonstrated a high capability for accurately classifying normal and abnormal lesions.

Conclusions:

  • The developed CAD system shows significant promise for accurate and efficient breast cancer lesion detection in mammography.
  • Feature selection proved crucial in optimizing classifier performance, leading to perfect classification accuracy.
  • The study highlights the potential of AI-driven tools to improve breast cancer screening and diagnosis.