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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1501742
This article is part of the Research Topic Advancing Cancer Imaging Technologies: Bridging the Gap from Research to Clinical Practice View all 7 articles

DenseIncepS115: A Novel Network-Level Fusion Framework for Alzheimer's Disease Prediction Using MRI Images

Provisionally accepted
Fatima Rauf Fatima Rauf 1Muhammad Attique Khan Muhammad Attique Khan 2*Ghassen Ben Brahim Ghassen Ben Brahim 2Wardah Abrar Wardah Abrar 1Areej Alasiry Areej Alasiry 3Mehrez Marzougui Mehrez Marzougui 3Seob Jeon Seob Jeon 4Yunyoung Nam Yunyoung Nam 4*
  • 1 HITEC University, Taxila, Punjab, Pakistan
  • 2 Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
  • 3 King Khalid University, Abha, Saudi Arabia
  • 4 Soonchunhyang University, Asan, South Chungcheong, Republic of Korea

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

    One of the most prevalent disorders relating to neurodegenerative conditions and dementia is Alzheimer's disease (AD). In the age group 65 and older, the prevalence of Alzheimer's disease is increasing. Before symptoms showed up, the disease had grown to a severe stage and resulted in an irreversible brain disorder that is not treatable with medication or other therapies. Therefore, early prediction is essential to slow down AD progression. Computer-aided diagnosis systems can be used as a second opinion by radiologists in their clinics to predict AD using MRI scans. In this work, we proposed a novel deep learning architecture named DenseIncepS115for for AD prediction from MRI scans. The proposed architecture is based on the Inception Module with Self-Attention (InceptionSA) and the Dense Module with Self-Attention (DenseSA). Both modules are fused at the network level using a depth concatenation layer. The proposed architecture hyperparameters are initialized using Bayesian Optimization, which impacts the better learning of the selected datasets. In the testing phase, features are extracted from the depth concatenation layer, which is further optimized using the Catch Fish Optimization (CFO) algorithm and passed to shallow wide neural network classifiers for the final prediction. In addition, the proposed DenseIncepS115 architecture is interpreted through Lime and Gradcam explainable techniques. Two publicly available datasets were employed in the experimental process: Alzheimer's ADNI and Alzheimer's classes MRI. On both datasets, the proposed architecture obtained an accuracy of 99.5% and 98.5%, respectively. Detailed ablation studies and comparisons with state-of-the-art techniques show the proposed architecture outperforms.

    Keywords: Neuroimaging, Alzheimer's disease, MRI, network-level fusion, Multiscale Inception Module, Dementia Stages Classification, Optimization, Shallow Neural Network

    Received: 25 Sep 2024; Accepted: 11 Nov 2024.

    Copyright: © 2024 Rauf, Khan, Brahim, Abrar, Alasiry, Marzougui, Jeon and Nam. 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:
    Muhammad Attique Khan, Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
    Yunyoung Nam, Soonchunhyang University, Asan, 336-745, South Chungcheong, Republic of Korea

    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.