Brain Abnormality Detection by Deep Convolutional Neural Network
This work addresses medical image analysis for brain disease diagnosis, but appears incremental as it applies standard deep learning methods to a specific dataset.
The paper tackles brain abnormality detection from MR images by developing a deep convolutional neural network with 10 layers, achieving 95.7% accuracy in classifying five categories including gliomas, multiple sclerosis, Alzheimer's, and healthy cases.
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.