A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images-Reference-Cited by-同舟云学术

A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images

Published:2023 Issue: Volume:11 Page:46283-46296
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hafeez Hafiz Aamir¹,Elmagzoub Mohamed A.²^ORCID,Abdullah Nurul Azma Binti¹^ORCID,Reshan Mana Saleh Al³^ORCID,Gilanie Ghulam⁴^ORCID,Alyami Sultan⁵^ORCID,Hassan Mahmood Ul⁶^ORCID,Shaikh Asadullah³^ORCID

Affiliation:

1. Department of Computer Science, University Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia

2. Department of Network and Communication Engineering, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia

3. Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia

4. Department of Artificial Intelligence, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

5. Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia

6. Department of Computer Skills, Deanship of Preparatory Year, Najran University, Najran, Saudi Arabia

Funder

Research Groups Funding program

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/ielx7/6287639/10005208/10119150.pdf?arnumber=10119150

Reference25 articles.

1. Glioma tumor grade identification using artificial intelligent techniques;muneer;J Med Syst,2019

2. Automated glioma grading on conventional MRI images using deep convolutional neural networks

3. Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning

4. Improved Glioma Grading Using Deep Convolutional Neural Networks

5. CGHF: A Computational Decision Support System for Glioma Classification Using Hybrid Radiomics- and Stationary Wavelet-Based Features

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