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.

本研究旨在使用神经网络模型对 MRI 数据集中三种不同类型的脑肿瘤（脑膜瘤、胶质瘤和垂体瘤）进行自动分割及识别，实验结果表明，采用了循环残差 U-Net 模型能达到平均交并比 0.8665，相比其它深度学习模型具有明显的优势。

基于深度学习技术的MRI图像脑肿瘤分割