Improved segmentation of brain tumors with data augmentation using MU-Net

Abstract

Semantic segmentation is crucial for the treatment and prevention of brain cancers. Several neural network–based strategies were rapidly presented by research groups to enhance brain tumor thread segmentation. The tumor’s uneven form necessitates the usage of neural networks for its detection. Therefore, improved patient outcomes may be achieved with precise segmentation of brain tumor. Brain tumors can range widely in size, form, and position, making diagnosis difficult. Thus, this work offers a Multi-level U-Net (MU-Net) approach for analyzing the brain tumor data augmentation for improved segmentation. Therefore, a significant amount of data augmentation is employed to successfully train the recommended system, removing the problem of a lack of data when using MR images for the diagnosis of multi-grade brain cancers. Here, we presented the “Multi-Level Pyramidal Pooling (MLPP)” component, where a new pyramidal pool will be employed to capture contextual data for augmentation. The “High-Grade Glioma” (HGG) datasets from the Kaggle and BraTs2021 were used to assess the proposed MU-Net. Overall Tumor (OT), Enhancing Core (EC), and Tumor Core (TC) were the three main designations to be segmented. The dice score was used to contrast the results empirically. The suggested MU-Net fared better than most existing methods. Researchers in the fields of bioinformatics and medicine might greatly benefit from the high-performance MU-Net.