A Novel Activation Function for Brain Tumor Segmentation using V-NET Approach

Abstract

This work emphasizes on automatic brain tumor segmentation from three dimensional magnetic resonance imaging (3D-MRI) scan images. We have used fully convolutional neural networks (FCNN) for extracting whole tumor. The proposed architecture is based on V-Net architecture. We have developed a new activation function for training the network. ReLU is used and in experiment 2, proposed activation function is used. We have conducted two experiments for brain tumor segmentation by varying the activation functions. In experiment 1, we have also used dice loss as loss function. The proposed method is trained and tested using BraTs 2021 training dataset which contains 2000 volumes of 3D-MRI scans. The proposed method’s performance is closer to the manually segmented images by experienced neurologists available with BraTs 2021 datasets. We have obtained a mean dice score of whole tumor as 99% while using proposed activation function which is higher when compared to the existing methods. We conclude that our novel activation function proposed activation function produced enhanced accuracy and dice score when compared with ReLU activation function.