@Article{cmes.2025.065564,
AUTHOR = {Walaa N. Ismail, Fathimathul Rajeena P. P., Mona A. S. Ali},
TITLE = {Towards Addressing Challenges in Efficient Alzheimer’s Disease Detection in Limited Resource Environments},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {143},
YEAR = {2025},
NUMBER = {3},
PAGES = {3709--3741},
URL = {http://www.techscience.com/CMES/v143n3/62833},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2025.065564}
}