Alzheimer’s Disease Early-Stage Prediction Model Using MRI Biomarkers and Deep Learning Networks

Abstract: Alzheimer's disease is an age-related neurological disorder that affects the brain, sequentially depriving people of their memory, thinking skills, and capabilities to handle everyday tasks. Early-stage detection is necessary for impactful clinical management and for prolonging disease progression. This study offers a new method using deep learning technology to recognize Alzheimer's in its early stages by evaluating structural brain scans taken with Magnetic Resonance Imaging (MRI). The offered system utilizes a 3D Convolution Neural Network (3D- CNN) to explicitly process MRI datasets and instantly extract meaningful morphological features connected with early cognitive dysfunction. To increase diagnostic validity, MRI data preparation techniques such as noise filtering and image standardization are applied before model training. Through in-depth validation, the model confirmed it can accurately tell the variation between healthy people and those in the beginning phase of Alzheimer's. This self-operating system can operate as a decision support system for clinicians, helping in early detection and supporting quick response approaches for Alzheimer's disease

Keywords: Neurodegenerative Disorders, Cerebral Magnetic Resonance Imaging (MRI), Artificial Neural Networks, Three- Dimensional Convolutional Neural Networks, Computational Medical Imaging, Early detection of Cognitive Decline.