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Optimizing Alzheimer's Disease Detection: An Enhanced Approach Weight-based Beetle Swarm Optimization with SVM

by Eman M. Ali, Mohamed A. Mahfouz, Howida Y Abd ElNaby
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 61
Year of Publication: 2025
Authors: Eman M. Ali, Mohamed A. Mahfouz, Howida Y Abd ElNaby
10.5120/ijca2025924386

Eman M. Ali, Mohamed A. Mahfouz, Howida Y Abd ElNaby . Optimizing Alzheimer's Disease Detection: An Enhanced Approach Weight-based Beetle Swarm Optimization with SVM. International Journal of Computer Applications. 186, 61 ( Jan 2025), 17-27. DOI=10.5120/ijca2025924386

@article{ 10.5120/ijca2025924386,
author = { Eman M. Ali, Mohamed A. Mahfouz, Howida Y Abd ElNaby },
title = { Optimizing Alzheimer's Disease Detection: An Enhanced Approach Weight-based Beetle Swarm Optimization with SVM },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2025 },
volume = { 186 },
number = { 61 },
month = { Jan },
year = { 2025 },
issn = { 0975-8887 },
pages = { 17-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number61/optimizing-alzheimers-disease-detection-an-enhanced-approach-weight-based-beetle-swarm-optimization-with-svm/ },
doi = { 10.5120/ijca2025924386 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-28T19:07:03+05:30
%A Eman M. Ali
%A Mohamed A. Mahfouz
%A Howida Y Abd ElNaby
%T Optimizing Alzheimer's Disease Detection: An Enhanced Approach Weight-based Beetle Swarm Optimization with SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 61
%P 17-27
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is essential for radiologists to identify Alzheimer's disease early to ensure accurate diagnosis and access to treatment. Medical imaging, such as Magnetic Resonance Imaging (MRI), are becoming increasingly difficult to diagnose. This study sought to develop a hybrid framework to use MRI scans to detect Alzheimer's disease. The suggested approach entails applying adaptive median filtering as a pre-processing step to MRI scans, extracting features based on hybrid wavelet partial Hadamard transform (hybrid WPHT) and discrete local binary pattern (DLBP). The next step in the feature selection process is to reduce the dimensionality of the features using the adaptive Harris-Hawk optimization (AHHO) approach. This greatly enhances the performance of the classifier by further refining its parameters using the Improved Weight-Based Beetle Swarm algorithm (IW-BS) and the Optimized Support Vector Machine (OSVM) classification. The MRI image classifies as Mild, Very Mild, or Normal. The results of the study demonstrate that this proposed methodology is more accurate, precision, and recall, specificity, F-score, running time, under the curve (AUC), and receiver operating characteristics (ROC)

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Index Terms

Computer Science
Information Sciences

Keywords

Magnetic Resonance Imaging Optimal Support Vector Machine Hyper-Parameter Tuning Feature Extraction Kernel Parameter Hybrid Wavelet.

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