Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images.

Journal: PloS one
Published Date: Jul 26, 2019

Abstract

INTRODUCTION: Ovarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for assessment of ovarian pathology and for preoperative distinction between malignant and benign ovarian tumors. However, ultrasonography is highly examiner-dependent and there may be an important variability between two different specialists when examining the same case. The objective of this work is the evaluation of different well-known Machine Learning (ML) systems to perform the automatic categorization of ovarian tumors from ultrasound images.

Authors

  • José Martínez-Más
    Department of Obstetrics and Gynecology, Virgen de la Arrixaca University Clinic Hospital, Murcia, Spain.
  • Andrés Bueno-Crespo
    Department of Computer Science, Catholic University of Murcia (UCAM), Murcia, Spain.
  • Shan Khazendar
    Department of Administration and Economics, University of Sulaimani, Kurdistan, Iraq.
  • Manuel Remezal-Solano
    Department of Obstetrics and Gynecology, Virgen de la Arrixaca University Clinic Hospital, Murcia, Spain.
  • Juan-Pedro Martínez-Cendán
    Department of Medicine; Obstetrics and Gynecology, Catholic University of Murcia (UCAM), Murcia, Spain.
  • Sabah Jassim
    Department of Applied Computing, Buckingham University, Buckingham, United Kingdom.
  • Hongbo Du
    Department of Applied Computing, Buckingham University, Buckingham, United Kingdom.
  • Hisham Al Assam
    Department of Applied Computing, Buckingham University, Buckingham, United Kingdom.
  • Tom Bourne
    Department of Cancer and Surgery, Queen Charlotte's and Chelsea Hospital, Imperial College, London, United Kingdom.
  • Dirk Timmerman

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