Analysis of Machine Learning and Deep Learning Algorithms for Detection of Brain Disorders Using MRI Data

Abstract

Brain diseases impact more than 1 billion people worldwide and include a wide spectrum of diseases and disorders such as stroke, Alzheimer’s, Parkinson’s, Epilepsy and other Seizure disorders. Most of these brain illnesses are subjected to misclassification, and early diagnosis increases the possibilities of preventing or delaying the development of these disorders. Magnetic Resonance Imaging (MRI) plays an important role in the diagnosis of patients with brain disorders and offers the potential of non-invasive longitudinal monitoring and bio-markers of disease progression. Our work focuses on using machine learning and deep learning techniques for the preemptive diagnosis of Schizophrenia using Kaggle data set and Alzheimer’s using TADPOLE data set comprising of MRI features. Since the number of works using TADPOLE data set is minimum, we have chosen this for our study. Machine learning algorithms such as support vector machine (SVM), Decision Tree, Random Forest, Gaussian Naive Bayes, and 1D-CNN deep learning algorithm have been used for the classification of the disorders. It has been observed that Gaussian NB performed the best on Schizophrenia data, while Random Forest outperformed on Alzheimer’s data compared to the other classifiers.