A Review: Brain Imaging For Neurological Disorder

Brain imaging refers to various techniques that allow the visualization and examination of the structure, function, and activity of the brain. These imaging methods play a crucial role in both clinical and research settings, providing valuable insights into neurological disorders. This research primarily involves brain imaging techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), functional Magnetic Resonance Imaging (fMRI), and Positron Emission Tomography (PET). The study explores machine learning and deep learning algorithms, along with pre-processing, image segmentation, and feature extraction techniques. A combination of machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and deep learning models like Convolutional Neural Networks (CNN), is utilized to classify brain imaging data into various neurological disorder categories. The CNN model is particularly effective in processing MRI and CT images due to its ability to automatically extract hierarchical features from complex data, for functional data like fMRI, multi-layer perceptrons (MLPs) and Recurrent Neural Networks (RNN) are used to capture temporal dependencies in brain activity patterns. U-Net, a deep learning architecture for image segmentation is employed to segment the brain and highlight regions of interest for more focused analysis. Alzheimer’s disease, Parkinson’s disease, and stroke are three common neurological disorders characterized by distinct sets of symptoms and mechanisms. Early diagnosis, risk management, and ongoing research are essential to improving outcomes and quality of life for affected individuals. The evolution of brain imaging techniques signifies a substantial advancement in comprehending, diagnosing, and treating neurological disorders such as Parkinson’s, Alzheimer’s, and brain stroke. These modalities not only facilitate early detection and precise characterization of neurological conditions but also play a pivotal role in monitoring disease progression over time.