PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
Por um escritor misterioso
Last updated 15 abril 2025
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://d3i71xaburhd42.cloudfront.net/c750894747d2b3f841de55922b2b68794295de27/7-Table3-1.png)
A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model that reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively.
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://ars.els-cdn.com/content/image/1-s2.0-S0010482520301335-gr1.jpg)
Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images - ScienceDirect
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://ijritcc.org/public/journals/1/submission_6951_6897_coverImage_en_US.png)
Automated Brain Tumor Detection from MRI Scans using Deep Convolutional Neural Networks
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://static.hindawi.com/articles/jhe/volume-2021/6695108/figures/6695108.fig.003.jpg)
A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://www.mdpi.com/electronics/electronics-09-02203/article_deploy/html/images/electronics-09-02203-g001.png)
Electronics, Free Full-Text
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://www.medrxiv.org/content/medrxiv/early/2022/11/04/2022.11.03.22281923/F1.large.jpg)
Comparing 3D, 2.5D, and 2D Approaches to Brain Image Segmentation
GitHub - Muthu2093/U-Net-for-Brain-Tumor-Segmentation: Trained a CNN based on U-Net Architecture fro segmenting Brain Tumors in MRI Scans
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://content.iospress.com/media/xst/2023/31-1/xst-31-1-xst221240/xst-31-xst221240-g013.jpg)
ResNet-SVM: Fusion based glioblastoma tumor segmentation and classification - IOS Press
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://media.springernature.com/m685/springer-static/image/art%3A10.1186%2Fs12859-021-04347-6/MediaObjects/12859_2021_4347_Fig1_HTML.png)
MRI-based brain tumor segmentation using FPGA-accelerated neural network, BMC Bioinformatics
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://cdn.slidesharecdn.com/ss_thumbnails/393presentationslide-221219073445-63625143-thumbnail.jpg?width=640&height=640&fit=bounds)
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://www.mdpi.com/diagnostics/diagnostics-13-01624/article_deploy/html/images/diagnostics-13-01624-g010.png)
Diagnostics, Free Full-Text
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://file.techscience.com/ueditor/files/iasc/TSP_IASC-32-1/TSP_IASC_21206/TSP_IASC_21206/Images/IASC_21206-fig-2.png/mobile_webp)
Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification
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