Detection and Segmentation of Glioma Tumors Utilizing a UNet Convolutional Neural Network Approach with Non-Subsampled Shearlet Transform-Reference-Cited by-同舟云学术

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Detection and Segmentation of Glioma Tumors Utilizing a UNet Convolutional Neural Network Approach with Non-Subsampled Shearlet Transform

Published:2024-08-01 Issue:8 Volume:31 Page:757-768
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Tamilarasi M.¹,Kumarganesh S.²,Sagayam K. Martin³,Andrew J.⁴^ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, Sasurie College of Engineering, Tirupur, India.

2. Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem, India.

3. Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.

4. Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.

Publisher

Mary Ann Liebert Inc

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2023.0339

Reference21 articles.

1. Combined Features in Region of Interest for Brain Tumor Segmentation

2. A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

3. Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

4. Convolutional neural networks for brain tumour segmentation

5. Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network

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