Year: 2017
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Rezaei, Mina, Yang, Haojin, Meinel, Christoph
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2747145494