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ORIGINAL RESEARCH article

Front. Med.
Sec. Pathology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1520878
This article is part of the Research Topic Artificial Intelligence-Assisted Medical Imaging Solutions for Integrating Pathology and Radiology Automated Systems - Volume II View all 9 articles

Early detection of Alzheimer's disease in Structural and Functional MRI

Provisionally accepted
Rudrani Maity Rudrani Maity 1Raja Sankari V.M. Raja Sankari V.M. 1Snekhalatha Umapathy Snekhalatha Umapathy 1,2*Shubashini Velu Shubashini Velu 3Tahani Alahmadi Tahani Alahmadi 4Zaid Ali Alhababi Zaid Ali Alhababi 5Hend Khalid Alkahtani Hend Khalid Alkahtani 4
  • 1 SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
  • 2 Batangas State University, Batangas City, Philippines
  • 3 Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
  • 4 Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
  • 5 Riyadh First Health Cluster, Riyadh, Saudi Arabia

The final, formatted version of the article will be published soon.

    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. Method: OpenNeuro and Harvard's Data verse provides Alzheimer's coronal functional MRI data. Ventricles and hippocampus are segmented using a Deep-Residual-UNet and Deep labV3+ system. The functional features were extracted from each segmented component and classified using SVM, Adaboost, Logistic regression, and VGG 16, DenseNet-169, VGG-16-RF, and VGG-16-SVM classifier.Result: This research proposes a precise and efficient deep-learning architecture like DeepLab V3+ and Deep Residual U-NET for hippocampus and ventricle segmentation in detection of AD. DeepLab V3+ has produced a good segmentation accuracy of 94.62% with Jaccard coefficient of 85.5% and dice co-efficient of 84.75%. Among the three ML classifiers used, SVM has provided a good accuracy of 93%. Among some DL techniques, VGG-16-RF classifier has given better accuracy of 96.87%.The novelty of this work lies in the seamless integration of advanced segmentation techniques with hybrid classifiers, offering a robust and scalable framework for early AD detection. The proposed study demonstrates a significant advancement in the early detection of Alzheimer's disease by integrating state-of-the-art deep learning models and comprehensive functional connectivity analysis. This early detection capability is crucial for timely intervention and better management of the disease in neurodegenerative disorder diagnostics.

    Keywords: Alzheimer's disease, fMRI, DeepLabV3+, deep residual U-Net, feature extraction

    Received: 31 Oct 2024; Accepted: 02 Dec 2024.

    Copyright: © 2024 Maity, V.M., Umapathy, Velu, Alahmadi, Alhababi and Alkahtani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Snekhalatha Umapathy, SRM Institute of Science and Technology, Chennai, 600 033, Tamil Nadu, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.