Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

TL;DR

This paper presents an improved U-Net model for brain tumor segmentation from MRI images, demonstrating high accuracy across multiple BraTS datasets and reducing computational time through background exclusion.

Contribution

An enhanced 2D U-Net architecture tailored for brain tumor segmentation, with empirical validation on multiple BraTS datasets showing consistent high performance.

Findings

Achieved dice scores of 0.8717, 0.9506, and 0.9427 for different tumor regions.

Model maintains high accuracy across datasets from 2017 to 2020.

Reduced computational time by excluding background details.

Abstract

Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).