# Brain Abnormality Detection by Deep Convolutional Neural Network

arXiv: https://arxiv.org/abs/1708.05206 · HTML: https://mlsanity.com/paper/1708.05206/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision](https://mlsanity.com/paper/2209.02459/) (2022)

## Abstract (arXiv)

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.