Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images

Abstract

The uncontrolled growth of cells in human brain can lead to the formation of tumors, which can occur in all age people. The tumor in brain can affect nerve cells, soft tissues and blood vessels. The early detection of brain tumor is necessary to aid doctors in treating cancer patients to increase their survival rate. For this various deep learning models are created and discovered for efficient brain tumor detection and classification. In this paper, the Convolutional Neural Network is proposed for efficient brain tumor classification in MRI images using BRATS 2019, 2020 and 2021 dataset. The min-max normalization is used in this research for data preprocessing and fed to the segmentation process. The mask region-based CNN is employed for segmenting brain tumors; Followed by that, Batch normalization is applied to enhance the training process and minimize the overfitting issues. The obtained result shows that the proposed CNN model achieves better accuracy of 99.55% on BRATS 2019, 99.80% on BRATS 2020 and 99.29% on BRATS 2021 dataset which ensures accurate classification compared with other existing methods like 3D U-Net and CapsNet + latent-dynamic condition random field (LDCRF) + post-processing.