Early detection of Alzheimer's disease using deep learning methods

INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep learning frameworks to improve predictive accuracy. METHODS: A novel framework was developed, including trained models for structured data and magnetic resonance images. The structured data model used a long short-term memory (LSTM) for temporal dependencies and a feedforward neural network (FNN) for static patterns. The MRI-based model employed ResNet50 and MobileNetV2 to extract spatial features. Models were applied on National Alzheimer's Coordinating Centre (NACC) and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets and compared to previous works. RESULTS: The MRI-based model achieved 96.19% accuracy on the ADNI dataset, while the hybrid model attained 99.82% accuracy on NACC dataset. DISCUSSION: This study highlights hybrid AI models' potential in AD detection, enabling earlier interventions and improved detection outcomes. HIGHLIGHTS: AI models were explored for early AD detection using NACC and ADNI datasets. Achieved high accuracy with LSTM on NACC data, showing potential for early AD diagnosis. Evaluated transfer learning models (MobileNetV2, ResNet-50) to address data limitations. A method is proposed for the careful validation of transfer learning models in medical brain diagnostics.