K-Net-Deep joint segmentation with Taylor driving training optimization based deep learning for brain tumor classification using MRI-Reference-Cited by-同舟云学术

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K-Net-Deep joint segmentation with Taylor driving training optimization based deep learning for brain tumor classification using MRI

Published:2023-05-16 Issue: Volume: Page:1-21
ISSN:1368-2199
Container-title:The Imaging Science Journal
language:en
Short-container-title:The Imaging Science Journal

Author:

Prasad Vadamodula¹,S Vairamuthu²,B Selva Rani³

Affiliation:

1. Department of ComputerScience and Engineering, Lendi Institute of Engineering and Technology, Jonnada Village, Vizianagaram District, India

2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

3. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

Publisher

Informa UK Limited

Subject

Computer Vision and Pattern Recognition,Media Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/13682199.2023.2208963

Reference44 articles.

1. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary

2. Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

3. Brain tumor classification for MR images using transfer learning and fine-tuning

4. BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification

5. Deep learning based brain tumor segmentation: a survey

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A hybrid deep CNN model for brain tumor image multi-classification;BMC Medical Imaging;2024-01-19

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