Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.

Journal: BMC medical imaging
PMID: 39707207

Abstract

INTRODUCTION: Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-active MS lesions from the T2-weighted MRI images has been investigated in this study.

Authors

  • Atefeh Rostami
    Department of Medical Physics and Radiological Sciences, Sabzevar University of Medical Sciences, Sabzevar, Iran.
  • Mostafa Robatjazi
    Department of Medical Physics and Radiological Sciences, Sabzevar University of Medical Sciences, Sabzevar, Iran. Robatjazi1361@gmail.com.
  • Amir Dareyni
    Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.
  • Ali Ramezan Ghorbani
    Department of Radiology, Rasoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran.
  • Omid Ganji
    Department of MRI, Sina Hospital, Tehran University of Medical Sceinces, Tehran, Iran.
  • Mahdiye Siyami
    Student Research Committee, Sabzevar University of Medical Sciences, Sabzevar, Iran.
  • Amir Reza Raoofi
    Department of Anatomy, Sabzevar University of Medical Sciences, Sabzevar, Iran.

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