An Enhanced Strategy of Detecting Neurological Disorders from Magnetic Resonance Images Using Deep Learning

Shaykh Siddique, Shahana Shultana, Sherri Frizell

Abstract: In neuroimaging techniques, deep learning technologies are used to analyze brain functionalities and extract beneficial features. Specifically, magnetic resonance images (MRI) and computed tomography (CT) scans are utilized to classify neurological diseases with various static and deep learning approaches. This paper describes the architecture of the deep learning system for detecting neurological disorders from MRI images. The system has been implemented with multiple layers of deep convolutional neural networks. An optimization method, grey wolf optimization, is used for tuning the hyperparameters. Other existing models for medical image classification are compared with the system we designed, and our system outperforms all for this particular dataset. The system can successfully detect the six most common neurological disorders, including Cerebral Aneurysm, Alzheimer's disease, Parkinson's disease, brain stroke, and schizophrenia.