Brain tumor segmentation using a deep Shuffled‐YOLO network

Abstract

AbstractBrain tumor is an anomalous proliferation of cells in the brain that can evolve to malignant and benign tumors. Currently, segmentation of brain tumor is the most important surgical and pharmaceutical procedures. However, manually segmenting brain tumors is hard because it is hard to find erratically shaped tumors with only one modality; the MRI modalities are integrated to provide multi‐modal images with data that can be utilized to segment tumors. The recent developments in machine learning and the accessibility of medical diagnostic imaging have made it possible to tackle the challenges of segmenting brain tumors with deep neural networks. In this work, a novel Shuffled‐YOLO network has been proposed for segmenting brain tumors from multimodal MRI images. Initially, the scalable range‐based adaptive bilateral filer (SCRAB) pre‐processing technique was used to eliminate the noise artifacts from MRI while preserving the edges. In the segmentation phase, we propose a novel deep Shuffled‐YOLO architecture for segmenting the internal tumor structures that include non‐enhancing, edema, necrosis, and enhancing tumors from the multi‐modality MRI sequences. The experimental fallouts reveal that the proposed Shuffled‐YOLO network achieves a better accuracy range of 98.07% for BraTS 2020 and 97.04% for BraTS 2019 with very minimal computational complexity compared to the state‐of‐the‐art models.