Automated Segmentation of Brain Tumor MRI Images Using Deep Learning-Reference-Cited by-同舟云学术

Automated Segmentation of Brain Tumor MRI Images Using Deep Learning

Published:2023 Issue: Volume:11 Page:64758-64768
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Rajendran Surendran¹,Rajagopal Suresh Kumar²^ORCID,Thanarajan Tamilvizhi³^ORCID,Shankar K.¹^ORCID,Kumar Sachin⁴^ORCID,Alsubaie Najah M.⁵,Ishak Mohamad Khairi⁶^ORCID,Mostafa Samih M.⁷^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India

2. Center for System Design, Chennai Institute of Technology, Chennai, India

3. Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, India

4. Big Data and Machine Learning Laboratory, South Ural State University, Chelyabinsk, Russia

5. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

6. School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Penang, Malaysia

7. Computer Science Department, Faculty of Computers and Information, South Valley University, Qena, Egypt

Funder

Princess Nourah bint Abdulrahman University Researchers Supporting Project

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10156786.pdf?arnumber=10156786

Reference27 articles.

1. A review on brain tumor segmentation of MRI images

2. Volumetric Segmentation Using Shape Models In The Level Set Framework

3. Machine learning paradigm for dynamic contrast-enhanced MRI evaluation of expanding bladder

4. Improved Edge Detection Algorithm for Brain Tumor Segmentation

5. An electrostatic deformable model for medical image segmentation

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