ABSTRACT
The detection of Alzheimer's disease is a critical task in medical diagnostics due to its rapid progression and profound impact on cognitive function. Deep learning (DL) offers unprecedented capabilities in analyzing medical imaging data, particularly in neuroimaging like MRI scans. However, class imbalances within the dataset persist, making it difficult to discern intricate patterns indicative of Alzheimer's pathology. This project aims to overcome this challenge by integrating advanced DL techniques with data augmentation methodologies, specifically SMOTE. This strategic integration aims to overcome the limitations of class imbalances within the initial MRI dataset, enhancing the precision and reliability of the classification model. This interdisciplinary exploration aims to redefine Alzheimer's detection capabilities, enhancing diagnoses and offering the potential for broader applications in understanding and managing complex neurodegenerative disorders.
