SBERO: Skill Al-Biruni Earth Radius Optimization for Alzheimer's Disease Classification Using Magnetic Resonance Image.

Alzheimer's disease (AD) is the most prevalent form of dementia, characterized by progressive memory loss and cognitive decline, often affecting behavior and speech. Early detection of AD remains a challenge due to its symptomatic overlap with normal aging and other cognitive disorders, necessitating precise classification methods. This paper proposes a novel Skill Al-Biruni Earth Radius Optimization-enabled Deep Spiking Neural Network (SBERO_Deep SNN) for AD classification using magnetic resonance imaging (MRI). Initially, input MRI images undergo enhancement through thresholding transformations. The segmentation is done using UNeXT, which is optimized by the hybrid SBERO algorithm. The SBERO combines the Skill Optimization Algorithm (SOA) and Al-Biruni Earth Radius (BER). Statistical features, local binary patterns (LBP), and gradient directional patterns (GDP) are then extracted, and classification is performed using a Deep Spiking Neural Network (Deep SNN) trained with SBERO. The proposed method achieves 90.49% accuracy, 89.98% sensitivity, and 90.16% specificity, outperforming existing state-of-the-art techniques in AD classification. The qualitative analysis highlights the robustness of the model in differentiating AD from other cognitive disorders, particularly in early stages, providing a reliable tool for clinical diagnosis.