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CFS-SMO based classification of breast density using multiple texture models.

Vipul Sharma1, Sukhwinder Singh

  • 1UIET, Panjab University, Chandigarh, India, vipuls85@gmail.com.

Medical & Biological Engineering & Computing
|April 29, 2014
PubMed
Summary

Classifying breast tissue density is crucial for early breast cancer detection. A new hybrid method using correlation-based feature selection and sequential minimal optimization achieved 96.46% accuracy, aiding radiologists.

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Biomedical Engineering

Background:

  • Breast density is a significant risk factor for breast cancer growth.
  • High breast density can obscure abnormalities, leading to false negatives in mammograms.
  • Accurate breast tissue classification is essential for improving breast cancer diagnosis.

Purpose of the Study:

  • To develop an efficient hybrid scheme for classifying mammograms based on breast tissue density (fatty vs. dense).
  • To utilize texture analysis and feature selection for improved classification accuracy.
  • To evaluate the performance of the proposed CFS-SMO method against other classifiers.

Main Methods:

  • Texture analysis was performed on selected regions of interest from mammograms.
  • Correlation-based feature selection (CFS) was employed to reduce dimensionality and identify discriminative features.
  • Sequential Minimal Optimization (SMO) was used as the primary classification algorithm.
  • The proposed method was evaluated on 322 images from the mini-MIAS database.

Main Results:

  • The hybrid CFS-SMO approach achieved a highest accuracy of 96.46% for the two-class problem (fatty and dense).
  • The selected features by CFS demonstrated high performance when evaluated with various classifiers.
  • The CFS-SMO method achieved a sensitivity of 100%, outperforming other evaluated classifiers.

Conclusions:

  • The proposed CFS-SMO hybrid scheme is highly effective for classifying breast tissue density.
  • This method shows potential as a valuable tool for assisting radiologists in mammogram interpretation.
  • Accurate classification of breast density can significantly improve early breast cancer detection rates.