A Multi-class Image Classifier for Assisting in Tumor Detection of Brain Using Deep Convolutional Neural Network

Abstract

Segmentation of brain tumor is a very crucial task from the medical points of view, such as in surgery and treatment planning. The tumor can be noticeable at any region of the brain with various size and shape due to its nature, that makes the segmentation task more difficult. In this present work, we propose a patch-based automated segmentation of brain tumor using a deep convolutional neural network with small convolutional kernels and leaky rectifier linear units (LReLU) as an activation function. Present work efficiently segments multi-modalities magnetic resonance (MR) brain images into normal and tumor tissues. The presence of small convolutional kernels allow more layers to form a deeper architecture and less number of the kernel weights in each layer during training. Leaky rectifier linear unit (LReLU) solves the problem of rectifier linear unit (ReLU) and increases the speed of the training process. The present work can deal with both high- and low-grade tumor regions on MR images. BraTS 2015 dataset has been used in the present work as a standard benchmark dataset. The presented network takes T1, T2, T1c, and FLAIR MR images from each subject as inputs and produces the segmented labels as outputs. It is experimentally observed that the present work has obtained promising results than the existing algorithms depending on the ground truth.