Walaa N. Ismail1,2,#,*, Fathimathul Rajeena P. P.3,#, Mona A. S. Ali3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3709-3741, 2025, DOI:10.32604/cmes.2025.065564
- 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,… More >