Optimizing Brain Tumor Segmentation with Advanced Superpixel Techniques in Conjunction with Deep Learning Methods-Reference-Cited by-同舟云学术

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Optimizing Brain Tumor Segmentation with Advanced Superpixel Techniques in Conjunction with Deep Learning Methods

Published:2023-10-20 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT)
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Author:

Chythanya Kanegonda Ravi¹,S Aruna²,Karuppaiah Jayakumar³,Arul S. Benjamin⁴,Kumar Pareek Piyush⁵

Affiliation:

1. SR University,School of Computer Science & Artificial Intelligence,Warangal,India

2. SRMIST,Dept. of Computational Intelligence,Chennai,India

3. KIT- Kalaignarkarunanidhi Institute of Technology,Department of Biomedical Engineering,Coimbatore,India

4. School of Engineering and Technology Jeppiaar University,Chennai,India

5. Nitte Meenakshi Institute of Technology,Department of AI and ML,Bengaluru,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10391855/10391862/10393810.pdf?arnumber=10393810

Reference24 articles.

1. An efficient memory reserving-and-fading strategy for vector quantization based 3D brain segmentation and tumor extraction using an unsupervised deep learning network

2. Segmentation and classification of brain tumor using 3D-UNet deep neural networks

3. Brain Tumor Segmentation Using Partial Depthwise Separable Convolutions

4. Fine-grained action recognition using dynamic kernels

5. MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

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