Ensemble Technique for Brain Tumor Patient Survival Prediction-Reference-Cited by-同舟云学术

Ensemble Technique for Brain Tumor Patient Survival Prediction

Published:2024 Issue: Volume:12 Page:19285-19298
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Vinod D. S.¹^ORCID,Prakash S. P. Shiva¹^ORCID,AlSalman Hussain²^ORCID,Muaad Abdullah Y.³^ORCID,Heyat Md. Belal Bin⁴^ORCID

Affiliation:

1. Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, Karnataka, India

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

3. Department of Studies in Computer Science, University of Mysore, Mysuru, Karnataka, India

4. CenBRAIN Neurotech Center of Excellence,School of Engineering, Westlake University, Hangzhou, China

Funder

Researcher supporting project King Saud University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10416848.pdf?arnumber=10416848

Reference31 articles.

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2. Performance analysis of Brain Tumour Image Classification using CNN and SVM

3. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge;Bakas;arXiv:1811.02629,2018

4. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

5. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

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