Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI

Abstract

Accurate glioma detection using magnetic resonance imaging (MRI) is a complicated job. In this research, deep learning model is presented for glioma and stroke lesion detection. The proposed architecture consists of 14 layers. The first input layer is followed by three convolutional layers while 5th, 6th and 7th layers correspond to batch normalization, followed by next three layers of rectified linear unit (ReLU). Eleventh layer is average pooling 2D which is followed by fully connected (FC), softmax and classification layers respectively. The presented method is verified on six MICCAI databases namely multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016, 2017 and sub-acute ischemic stroke lesion segmentation (SISS-ISLES) 2015. The computational time is also measured across each benchmark dataset such as 53 s on BRATS 2013, 26 s on BRATS 2014, 41 s on BRATS 2015, 36 s on BRATS 2016, and 38 s on BRATS 2017 and 4.13 s on ISLES 2015 proving that the proposed technique has less processing time. The proposed method achieved 0.9943 ACC, 1.00 SP, 0.9839 SE on BRATS 2013, 0.9538 ACC, 0.9991 SP, 0.7196 SE on BRATS 2014, 0.9978 ACC, 1.00 SP, 0.9919 SE on BRATS 2015, 0.9569 ACC, 0.9491 SP, 0.9755 SE on BRAST 2016, 0.9778 ACC, 0.9770 SP, 0.9789 SE on BRATS 2017 and 0.9227 ACC, 1.00 SP, 0.8814 SP on ISLES 2015 datasets respectively.