AI-Powered Brain Imaging for Early Neurodegenerative Disease Detection

Neurodegenerative diseases, such as Alzheimer's, pose significant challenges to healthcare, underscoring the urgent need for precise and swift diagnostic tools. This research project aims to utilize Convolutional Neural Networks (CNN) alongside brain imaging methods to detect neurodegenerative diseases at an early stage. Drawing insights from existing literature, the objective is to devise an advanced system for timely detection. The endeavor begins with an extensive review of current research on neurodegenerative diseases, with a particular emphasis on the hippocampus role in Alzheimer's disease. The research study titled 'MRI of the hippocampus in the early stages of Alzheimer's disease' underscores the potential of magnetic resonance imaging (MRI) in early detection. Our project integrates sophisticated deep learning methodologies, including 3D CNNs and transfer learning, to augment diagnostic precision. Moreover, we introduce a pioneering approach, the Multiplane Convolutional Neural Network (MP-CNN), to leverage multiple MRI planes for a more comprehensive feature set. By combining machine learning, MRI, and deep learning, our project aims to advance early detection of neurodegenerative diseases, with a specific focus on Alzheimer's, offering a robust, effective, and efficient system.