Optimizing deep learning with improved Harris Hawks optimization for Alzheimer’s disease detection

Abstract

As the global population ages, Alzheimer’s disease (AD) poses a significant worldwide challenge as a leading cause of dementia, with a slow early progression that eventually leads to nerve cell death and currently lacks effective treatment. However, early diagnosis can slow its progression through pharmaceutical intervention, making accurate early diagnosis using computer-aided diagnosis (CAD) systems crucial. This study aims to enhance the accuracy of early AD diagnosis by developing an improved optimization approach for deep learning-based CAD systems. To achieve this, this paper proposes an improved Harris Hawks optimization algorithm (HHO), named CAHHO, which incorporates crisscross search and adaptive β-Hill climbing mechanisms, thereby enhancing population diversity and search space coverage during the exploration phase, while adaptively adjusting the step size during the exploitation phase to improve local search precision. Comparative experiments with classical algorithms, HHO variants, and advanced optimization methods validate the superiority of the proposed CAHHO. Specifically, this study employs the deep learning model residual network with 18 layers (ResNet18) as the base model for AD diagnosis and uses CAHHO to optimize key hyperparameters, including the number of channels and learning rate. Experiments on the AD neuroimaging initiative dataset demonstrate that the ResNet18-CAHHO model outperforms existing methods in classifying AD, mild cognitive impairment (MCI), and normal control (NC) subjects. Specifically, it achieves accuracies of 0.93077, 0.80102, and 0.80513 in the diagnosis of AD versus NC, MCI versus NC, and AD versus MCI, respectively. Furthermore, Gradient-Weighted Class Activation Mapping (Grad-CAM) visualizations reveal critical brain regions associated with AD, providing valuable diagnostic support for clinicians and holding significant promise for early intervention.