ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1558725
A novel deep learning technique for multi classify Alzheimer Disease: hyperparameter optimization technique
Provisionally accepted
- 1Faculty of Computers and Informatics, Zagazig University, Zagazig, Al Sharqia, Egypt
- 2Computers and Systems Dept., Electronics research Institute (ERI), Cairo, Egypt
- 3Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
- 4Department of Computer Science, Arab East Colleges, Riyadh, Saudi Arabia
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A progressive brain disease that affects memory and cognitive function is Alzheimer's disease (AD). To put therapies in place that potentially slow the progression of AD, early diagnosis and detection are essential. Early detection of these phases enables early activities, which are essential for controlling the disease. To address issues with limited data and computing resources, this work presents a novel deep-learning method based on using a newly proposed hyperparameter optimization method to identify the hyperparameters of ResNet152V2 model for classifying the phases of AD more accurately. The proposed model is compared to state-of-the-art models divided into two categories: transfer learning models and classical models to showcase its effectiveness and efficiency. This comparison is based on four performance metrics: recall, precision, F1 score, and accuracy. According to the experimental results, the proposed method is more efficient and effective in classifying various AD phases.
Keywords: Alzheimer's disease phases 1, Multi-classification2, deep learning 3, Hyperparameters 4, ResNet152V25
Received: 10 Jan 2025; Accepted: 07 Apr 2025.
Copyright: © 2025 AbdElmotelb, Sherif, ABOHAMAMA, Fakhr and Abdelatif. 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: Ahmed Salah AbdElmotelb, Faculty of Computers and Informatics, Zagazig University, Zagazig, Al Sharqia, Egypt
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.
