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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Applied Neuroimaging
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1490829

Vision Transformer-Equipped Convolutional Neural Networks for Automated Alzheimer's Disease Diagnosis Using 3D MRI Scans

Provisionally accepted
Zhen Zhao Zhen Zhao 1Pauline Yeoh Shan Qing Pauline Yeoh Shan Qing 1Xiaowei Zuo Xiaowei Zuo 2*Joon Huang Chuah Joon Huang Chuah 1*Chee-Onn Chow Chee-Onn Chow 1Xiang Wu Xiang Wu 3Khin Wee Lai Khin Wee Lai 1
  • 1 University of Malaya, Kuala Lumpur, Malaysia
  • 2 Department of Psychiatry, The Affliated Xuzhou Oriental Hospital of Xuzhou Medical University,, Xuzhou, Jiangsu, China
  • 3 School of Medicine Information and Engineering , Xuzhou Medical University, Xuzhou, Jiangsu Province, China

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

    Alzheimer's disease (AD) is a neurodegenerative ailment that is becoming increasingly common, making it a major worldwide health concern. Effective care depends on an early and correct diagnosis, but traditional diagnostic techniques are frequently constrained by subjectivity and expensive costs. This study proposes a novel Vision Transformer-equipped Convolutional Neural Networks (VECNN) that uses three-dimensional magnetic resonance imaging to improve diagnosis accuracy. Utilizing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which comprised 2,248 3D MRI images and diverse patient demographics, the proposed model achieved an accuracy of 92.14%, a precision of 86.84%, a sensitivity of 93.27%, and a specificity of 89.95% in distinguishing between AD, healthy controls (HC), and moderate cognitive impairment (MCI).The findings suggest that VECNN can be a valuable tool in clinical settings, providing a noninvasive, cost-effective, and objective diagnostic technique. This research opens the door for future advancements in early diagnosis and personalized therapy for Alzheimer's Disease.

    Keywords: Alzheimer's disease, Classification, Convolutional Neural Network, Magnetic Resonance Imaging, transformer

    Received: 06 Sep 2024; Accepted: 28 Nov 2024.

    Copyright: © 2024 Zhao, Qing, Zuo, Chuah, Chow, Wu and Lai. 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:
    Xiaowei Zuo, Department of Psychiatry, The Affliated Xuzhou Oriental Hospital of Xuzhou Medical University,, Xuzhou, Jiangsu, China
    Joon Huang Chuah, University of Malaya, Kuala Lumpur, Malaysia

    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.