Deep Learning Enhanced Neuro Imaging for Early Alzheimer's Risk Assessment

Alzheimer's Disease (AD) is a chronic neurological condition that necessitates early identification for optimal treatment. Conventional identification techniques rely on cognitive assessments and single-modality imaging, which often fail to detect early-stage abnormalities. This study proposes a deep learning-enhanced neuroimaging framework that integrates multimodal brain imaging techniques, including MRI and PET, to improve the timely identification and risk assessment of AD. The proposed approach utilizes CNNs and attention-based advanced architectures to extract subtle biomarkers from neuroimaging data, enabling accurate classification of preclinical and mild cognitive impairment (MCI) stages. A feature fusion strategy enhances the discriminative power of extracted features, leading to improved diagnostic performance. Experimental results on benchmark Alzheimer's datasets demonstrate that the suggested model obtains 95.3% accuracy, 94.7% sensitivity, and 93.9% specificity, outperforming traditional machine learning methods. This research highlights the potential of AI-driven neuroimaging to revolutionize early Alzheimer's detection, offering a non-invasive, automated, and highly reliable assessment tool for clinicians.