Early Detection of Alzheimer’s Disease via CNN-Based Feature Extraction and SVM Classification

Globally, Alzheimer's disease is an illness of the brain that slowly affects people over time. Recently, there is no cure known to us, and hence, identifying this disease at an early stage is crucial for offering proper support and care to the patient. MRI imaging is a valuable tool which is used to visualize changes in the brain without using any surgical procedures. However, many existing diagnostic approaches still rely on manual interpretation which is both time consuming and prone to inconsistencies. In our study, we introduce a hybrid model that uses CNN and SVM to classify stages of Alzheimer’s disease based on MRI brain scan images. Proposed framework has a 90% accuracy and aims to improve stage-specific detection of the disease. This approach offers a practical and scalable solution that could assist clinicians in early and reliable diagnosis of Alzheimer’s.