Open Access

ARTICLE

Towards Addressing Challenges in Efficient Alzheimer’s Disease Detection in Limited Resource Environments

Walaa N. Ismail^1,2,#,*, Fathimathul Rajeena P. P.^3,#, Mona A. S. Ali^3,*

1 Department of Management Information Systems, College of Business, Al Yamamah University, Riyadh, 11512, Saudi Arabia
2 Faculty of Computers and Information, Minia University, Minia, 61519, Egypt
3 Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa, 31982, Saudi Arabia

* Corresponding Authors: Walaa N. Ismail. Email: ; Mona A. S. Ali. Email:
#These authors contributed equally to this work

(This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)

Computer Modeling in Engineering & Sciences 2025, 143(3), 3709-3741. https://doi.org/10.32604/cmes.2025.065564

Received 16 March 2025; Accepted 29 May 2025; Issue published 30 June 2025

Abstract

Early detection of Alzheimer’s disease (AD) is crucial, particularly in resource-constrained medical settings. This study introduces an optimized deep learning framework that conceptualizes neural networks as computational “sensors” for neurodegenerative diagnosis, incorporating feature selection, selective layer unfreezing, pruning, and algorithmic optimization. An enhanced lightweight hybrid DenseNet201 model is proposed, integrating layer pruning strategies for feature selection and bioinspired optimization techniques, including Genetic Algorithm (GA) and Harris Hawks Optimization (HHO), for hyperparameter tuning. Layer pruning helps identify and eliminate less significant features, while model parameter optimization further enhances performance by fine-tuning critical hyperparameters, improving convergence speed, and maximizing classification accuracy. GA is also used to reduce the number of selected features further. A detailed comparison of six AD classification model setups is provided to illustrate the variations and their impact on performance. Applying the lightweight hybrid DenseNet201 model for MRI-based AD classification yielded an impressive baseline F1 score of 98%. Overall feature reduction reached 51.75%, enhancing interpretability and lowering processing costs. The optimized models further demonstrated perfect generalization, achieving 100% classification accuracy. These findings underscore the potential of advanced optimization techniques in developing efficient and accurate AD diagnostic tools suitable for environments with limited computational resources.

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Keywords

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