An Empirical Comparison of Classification Algorithms for Diagnosis of Depression from Brain SMRI Scans

To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed person from a healthy one at individual scan level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification of samples (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of their features. Thus far, very limited works have been reported on identifying a suitable classification algorithm for depression detection. In this paper, ten different types of classification algorithms are applied to depression diagnosis and their performance is compared, through a set of experiments on sMRI brain scans. In the experiments, a procedure is developed to measure the performance of these algorithms and an evaluation method is employed to evaluate and compare the performance of the classifiers.