Innovative Deep Learning Framework for Precise Alzheimer's Disease Detection Using Gray Matter Analysis in MRI Scans

Early detection of Alzheimer’s disease (AD) is critical for timely intervention and effective treatment strategies. This study presents an advanced deep learning framework that integrates convolutional neural networks (CNNs) with Statistical Parametric Mapping (SPM) toolbox preprocessing to classify brain MRI scans into three categories: Normal Control (NC), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). Unlike traditional methods relying on manual feature extraction, the proposed approach automatically identifies critical structural changes in the hippocampus and other gray matter regions. By converting three-dimensional brain scans into two-dimensional segments and leveraging the SPM toolbox for artifact removal and normalization, the CNN model achieves unparalleled accuracy of over 99%. Tested on the ADNI dataset, the model demonstrates superior efficiency in detecting early AD stages. This novel methodology not only outperforms existing algorithms but also underscores the potential of combining advanced imaging techniques with deep learning for transformative applications in medical diagnostics. Future work includes validating the framework across diverse datasets and integrating multimodal data to enhance its robustness and clinical impact.

Innovative Deep Learning Framework for Precise Alzheimer's Disease Detection Using Gray Matter Analysis in MRI Scans Keywords:

Alzheimer’s Disease Early Diagnosis , Gray Matter Segmentation , Convolutional Neural Networks (CNN) , MRI-based Classification , SPM Toolbox Preprocessing , Hippocampal Structural Analysis , Advanced Medical Imaging Techniques