Early Diagnosis of Alzheimer's Disease from MRI Images Using Machine Learning Approach

Alzheimer's disease is a neurodegenerative disorder characterized by cognitive decline, and early detection is crucial for timely intervention. The research focuses on processing MRI brain scans through various image processing techniques, such as segmentation and bicubic interpolation, to enhance visual clarity and extract meaningful features. Models such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) are employed for classification, with a particular emphasis on detecting early brain atrophy and vascular changes associated with Alzheimer's disease. The research demonstrates that combining MRI data with machine learning models can significantly improve the accuracy of early Alzheimer's diagnosis. This potentially aids in early intervention and better patient care management. Findings hold promise for advancing early diagnosis and treatment, enhancing AD patients' quality of life.