Brain Tumor Segmentation from MRI Images using Deep Learning Techniques

TL;DR

This study evaluates various deep learning models for brain tumor segmentation from MRI images, finding that Recurrent Residual U-Net with Adam optimizer achieves the highest accuracy, aiding faster diagnosis.

Contribution

It compares multiple advanced deep learning models for brain tumor segmentation, identifying the most effective architecture and parameters for accurate MRI analysis.

Findings

Recurrent Residual U-Net with Adam optimizer achieved a Mean IoU of 0.8665.

The models successfully segmented different tumor types with high accuracy.

Deep learning models can automate and improve brain tumor detection from MRI scans.

Abstract

A brain tumor, whether benign or malignant, can potentially be life threatening and requires painstaking efforts in order to identify the type, origin and location, let alone cure one. Manual segmentation by medical specialists can be time-consuming, which calls out for the involvement of technology to hasten the process with high accuracy. For the purpose of medical image segmentation, we inspected and identified the capable deep learning model, which shows consistent results in the dataset used for brain tumor segmentation. In this study, a public MRI imaging dataset contains 3064 TI-weighted images from 233 patients with three variants of brain tumor, viz. meningioma, glioma, and pituitary tumor. The dataset files were converted and preprocessed before indulging into the methodology which employs implementation and training of some well-known image segmentation deep learning models like U-Net & Attention U-Net with various backbones, Deep Residual U-Net, ResUnet++ and Recurrent Residual U-Net. with varying parameters, acquired from our review of the literature related to human brain tumor classification and segmentation. The experimental findings showed that among all the applied approaches, the recurrent residual U-Net which uses Adam optimizer reaches a Mean Intersection Over Union of 0.8665 and outperforms other compared state-of-the-art deep learning models. The visual findings also show the remarkable results of the brain tumor segmentation from MRI scans and demonstrates how useful the algorithm will be for physicians to extract the brain cancers automatically from MRI scans and serve humanity.