Early detection of Alzheimer's disease in structural and functional MRI.

Journal: Frontiers in medicine
Published Date: Dec 12, 2024

Abstract

OBJECTIVES: To implement state-of-the-art deep learning architectures such as Deep-Residual-U-Net and DeepLabV3+ for precise segmentation of hippocampus and ventricles, in functional magnetic resonance imaging (fMRI). Integrate VGG-16 with Random Forest (VGG-16-RF) and VGG-16 with Support Vector Machine (VGG-16-SVM) to enhance the binary classification accuracy of Alzheimer's disease, comparing their performance against traditional classifiers.

Authors

  • Rudrani Maity
    Department of Biomedical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
  • Vellupillai Mariappan Raja Sankari
    Department of Biomedical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
  • Umapathy Snekhalatha
    Department of Biomedical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
  • Shubashini Velu
    MIS Department, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
  • Tahani Jaser Alahmadi
    Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Zaid Ali Alhababi
    Riyadh First Health Cluster, Ministry of Health, Riyadh, Saudi Arabia.
  • Hend Khalid Alkahtani
    Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

Keywords

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