Multimedia Content Analysis for Alzheimer’s Disease Diagnosis using MRI Scans and Deep Learning

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with early diagnosis being crucial for effective intervention. Magnetic Resonance Imaging (MRI) is a valuable tool for detecting structural brain changes associated with AD. However, accurate and automated analysis of MRI scans remains a challenge due to the complexity and variability in brain structures. Traditional methods for analyzing MRI scans for AD diagnosis often rely on manual interpretation or basic image processing techniques, which can be time-consuming and prone to variability. There is a need for advanced automated methods that can accurately segment brain structures and extract relevant features for reliable diagnosis. This study proposes a novel approach for AD diagnosis using MRI scans, combining Conditional Attention U-Net for segmentation and Ant Colony Optimization (ACO) for feature extraction. The Conditional Attention U-Net enhances segmentation accuracy by incorporating conditional attention mechanisms to focus on relevant features while minimizing background noise. ACO is employed to optimize feature extraction by simulating the foraging behavior of ants, which efficiently selects and refines key features related to AD. The proposed model was evaluated on a dataset of 500 MRI scans, comparing performance with traditional methods using metrics such as Dice Similarity Coefficient (DSC) and classification accuracy. The Conditional Attention U-Net achieved an average DSC of 0.89 for segmentation of key brain regions, outperforming conventional methods by 10%. The ACO-enhanced feature extraction resulted in a classification accuracy of 92% for AD diagnosis, representing a 7% improvement over baseline methods. The combination of these techniques demonstrated a significant enhancement in both segmentation precision and diagnostic accuracy, showcasing the effectiveness of the proposed approach for early AD detection.