The Role of AI in Personalized Mental Health Tracking

Identifying Alzheimer’s disease (AD) at an early stage is essential for effective treatment, yet conventional diagnostic approaches are often time-consuming and subjective. This research utilizes deep learning techniques with AI, applying VGG16 and transfer learning to classify brain scans into three categories: Normal Cognitive Function (NCF), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). To enhance accuracy and minimize overfitting, the study incorporates data augmentation and optimization strategies. Findings indicate that AI-driven models can significantly improve early diagnosis while enabling personalized cognitive health tracking, making mental health monitoring more efficient and scalable.