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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

A comparison of methods for three-class mammograms classification.

Marina Milosevic¹, Zeljko Jovanovic¹, Dragan Jankovic²

¹Department of Computer Engineering, Faculty of Technical Sciences, University of Kragujevac, Cacak, Serbia.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|April 25, 2017

Summary

This study introduces a computer-aided diagnosis (CAD) system using texture features from mammograms. The Support Vector Machine (SVM) classifier showed better performance in detecting abnormal breast tissue compared to other methods.

Keywords:

Breast cancer ROC analysis cross-validation mammography multi-class classification texture analysis

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Machine Learning

Background:

Mammography is crucial for early breast cancer detection but interpretation challenges exist.
Computer-aided diagnosis (CAD) systems aim to enhance detection performance.
This study focuses on improving CAD for digital mammogram analysis.

Purpose of the Study:

To develop and evaluate a CAD system for detecting abnormal patterns in digital mammograms.
To assess the effectiveness of texture features extracted using Gray-Level Co-occurrence Matrices (GLCM).
To compare the performance of different machine learning classifiers for tissue classification.

Main Methods:

Extracted 20 texture features from mammograms using GLCM.
Utilized Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbor (k-NN) classifiers.
Employed cross-validation, confusion matrix, and ROC analysis for performance evaluation.
Developed a three-class SVM classifier for multi-class classification tasks.

Main Results:

The SVM classifier achieved a 65% accuracy rate, outperforming k-NN (51.6%) and Naive Bayes (38.1%).
Unbalanced classification was identified as a factor limiting overall accuracy.
The proposed three-class SVM demonstrated superior performance in differentiating tissue types.

Conclusions:

The developed three-class SVM classifier is a promising approach for practical CAD systems in mammography.
Texture analysis using GLCM features combined with SVM shows potential for improving breast cancer detection.
Further refinement is needed to address classification imbalance and enhance accuracy.