  In this paper, we describe our method for classification of brain magnetic
resonance (MR) images into different abnormalities and healthy classes based on
the deep neural network. We propose our method to detect high and low-grade
glioma, multiple sclerosis, and Alzheimer diseases as well as healthy cases.
Our network architecture has ten learning layers that include seven
convolutional layers and three fully connected layers. We have achieved a
promising result in five categories of brain images (classification task) with
95.7% accuracy.
