Deep Learning for Neurological Brain Disorder Classifications

Utilizing Convolutional Neural Networks (CNN), this research study has developed an advanced methodology to classify disorders such as Alzheimer's disease using medical imaging modalities like MRI and CT scans. Our method integrates multi-modal data collaboration by combining structural, functional, and diffusion imaging in order to build detailed connectivity and composition of the brain. We conducted large-scale experiments that proved the efficiency of the model in differentiating Alzheimer, Parkinson, multiple sclerosis, and epilepsy. Classification networks were also used for interpretability, where attention mechanisms addressed important features responsible for classification of the task in a mechanistic type of way. All of these results provide the way for better ways of classification and targeted therapies, eventually leading to better outcomes for the patients.