Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy.

Journal: Clinical imaging
Published Date: Sep 16, 2024

Abstract

OBJECTIVES: Alzheimer's disease (AD) is a common neurodegenerative disorder that primarily affects older individuals. Due to its high incidence, an accurate and efficient stratification system could greatly aid in the clinical diagnosis and prognosis of AD patients. Convolutional neural networks (CNN) approaches have demonstrated exceptional performance in the automated stratification of AD, mild cognitive impairment (MCI) and cognitively normal (CN) participants using MRI, owing to their high predictive accuracy and reliability. Therefore, we aimed to develop an algorithm based on CNN and radiomic features derived from ROIs of bilateral hippocampus and amygdala in brain MRI for stratification between AD, MCI and CN.

Authors

  • Amin Zarei
    IoT and Signal Processing Research Group, ICT Research Institute, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, Iran.
  • Ahmad Keshavarz
    Electrical Engineering Department, School of Engineering, Persian Gulf University, Bushehr, Iran.
  • Esmail Jafari
    The Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran.
  • Reza Nemati
    Department of Neurology, Bushehr Medical University Hospital, Faculty of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran.
  • Akram Farhadi
    The Persian Gulf Tropical Medicine Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran.
  • Ali Gholamrezanezhad
    Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA, USA.
  • Habib Rostami
    Computer Engineering Department, School of Engineering, Persian Gulf University, Bushehr, Iran. Electronic address: habib@pgu.ac.ir.
  • Majid Assadi
    Department of Nanotechnology, The Persian Gulf Nuclear Medicine Research Center, The Persian Gulf Biomedical Sciences Institute, Bushehr University of Medical Sciences, Bushehr, Iran.

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