Ultrasound Radio Frequency Time Series for Tissue Typing: Experiments on In-Vivo Breast Samples Using Texture-Optimized Features and Multi-Origin Method of Classification (MOMC).

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Published Date:

Abstract

OBJECTIVES: One of the most promising auxiliaries for screening breast cancer (BC) is ultrasound (US) radio-frequency (RF) time series. It has the superiority of not requiring any supplementary equipment over other methods. This article sought to propound a machine learning (ML) method for the automated categorization of breast lesions-categorized as benign, probably benign, suspicious, or malignant-using features extracted from the accumulated US RF time series.

Authors

  • Mahsa Arab
    Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
  • Ali Fallah
    Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran. afallah@aut.ac.ir.
  • Saeid Rashidi
    Faculty of Medical Sciences & Technologies, Science & Research Branch, Islamic Azad University, Tehran, Iran.
  • Maryam Mehdizadeh Dastjerdi
    Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
  • Nasrin Ahmadinejad
    Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences (TUMS), Tehran, Iran.

Keywords

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