Enhancing Brain Tumor Classification by a Comprehensive Study on Transfer Learning Techniques and Model Efficiency Using MRI Datasets-Reference-Cited by-同舟云学术

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Enhancing Brain Tumor Classification by a Comprehensive Study on Transfer Learning Techniques and Model Efficiency Using MRI Datasets

Published:2024 Issue: Volume:12 Page:100407-100418
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shamshad Nadia¹,Sarwr Danish¹^ORCID,Almogren Ahmad²^ORCID,Saleem Kiran¹^ORCID,Munawar Alia¹,Rehman Ateeq Ur³^ORCID,Bharany Salil⁴^ORCID

Affiliation:

1. School of Software, Dalian University of Technology, Dalian, China

2. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

3. School of Computing, Gachon University, Seongnam, Republic of Korea

4. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

Funder

King Saud University, Riyadh, Saudi Arabia, through the Researchers Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10600700.pdf?arnumber=10600700

Reference34 articles.

1. Brain Tumor Detection using MRI Images and Convolutional Neural Network

2. Predicting Lumbar Spondylolisthesis: A Hybrid Deep Learning Approach

3. Development of computer-aided approach for brain tumor detection using random forest classifier

4. A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network

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