School of Opto-Electronic and Communication Engineering
Xiamen University of Technology
China
Homepage
School of Opto-Electronic and Communication Engineering
Xiamen University of Technology
China
Homepage
School of Opto-Electronic and Communication Engineering
Xiamen University of Technology
China
Homepage1. Lee, Da Yong, "Roles of mTOR signaling in brain development," Experimental Neurobiology, Vol. 24, No. 3, 177, 2015.
2. Arabahmadi, Mahsa, Reza Farahbakhsh, and Javad Rezazadeh, "Deep learning for smart Healthcare --- A survey on brain tumor detection from medical imaging," Sensors, Vol. 22, No. 5, 1960, 2022.
3. Tandel, Gopal S., Ashish Tiwari, and Omprakash G. Kakde, "Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification," Computers in Biology and Medicine, Vol. 135, 104564, 2021.
4. Tiwari, Arti, Shilpa Srivastava, and Millie Pant, "Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019," Pattern Recognition Letters, Vol. 131, 244-260, 2020.
5. Raut, Gajendra, Aditya Raut, Jeevan Bhagade, Jyoti Bhagade, and Sachin Gavhane, "Deep learning approach for brain tumor detection and segmentation," 2020 International Conference on Convergence to Digital World --- Quo Vadis (ICCDW), 1-5, Mumbai, India, 2020.
6. Kang, Ming, Chee-Ming Ting, Fung Fung Ting, and Raphaël C.-W. Phan, "RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection," International Conference on Medical Image Computing and Computer-Assisted Intervention, 600-610, 2023.
7. Kang, Ming, Chee-Ming Ting, Fung Fung Ting, and Raphaël C.-W. Phan, "Bgf-yolo: Enhanced yolov8 with multiscale attentional feature fusion for brain tumor detection," ArXiv Preprint ArXiv:2309.12585, 2023.
8. Ismael, Sarah Ali Abdelaziz, Ammar Mohammed, and Hesham Hefny, "An enhanced deep learning approach for brain cancer MRI images classification using residual networks," Artificial Intelligence in Medicine, Vol. 102, 101779, 2020.
9. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox, "U-net: Convolutional networks for biomedical image segmentation," Medical Image Computing and Computer-Assisted Intervention --- MICCAI 2015, 234-241, 2015.
10. Amin, Javaria, Muhammad Sharif, Muhammad Almas Anjum, Mudassar Raza, and Syed Ahmad Chan Bukhari, "Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI," Cognitive Systems Research, Vol. 59, 304-311, 2020.
11. Çinar, Ahmet and Muhammed Yildirim, "Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture," Medical Hypotheses, Vol. 139, 109684, 2020.
12. Khan, Muhammad Attique, Imran Ashraf, Majed Alhaisoni, Robertas Damaševičius, Rafal Scherer, Amjad Rehman, and Syed Ahmad Chan Bukhari, "Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists," Diagnostics, Vol. 10, No. 8, 565, 2020.
13. Yang, Aimin, Xiaolei Yang, Wenrui Wu, Huixiang Liu, and Yunxi Zhuansun, "Research on feature extraction of tumor image based on convolutional neural network," IEEE Access, Vol. 7, 24204-24213, 2019.
14. Ke, Qiao, Jiangshe Zhang, Wei Wei, Robertas Damaševičius, and Marcin Woźniak, "Adaptive independent subspace analysis of brain magnetic resonance imaging data," IEEE Acces, Vol. 7, 12252-12261, 2019.
15. Thaha, M. Mohammed, K. Pradeep Mohan Kumar, B. S. Murugan, S. Dhanasekeran, P. Vijayakarthick, and A. Senthil Selvi, "Brain tumor segmentation using convolutional neural networks in MRI images," Journal of Medical Systems, Vol. 43, 1-10, 2019.
16. Li, Ming, Lishan Kuang, Shuhua Xu, and Zhanguo Sha, "Brain tumor detection based on multimodal information fusion and convolutional neural network," IEEE Access, Vol. 7, 180134-180146, 2019.
17. Yang, Yang, Lin-Feng Yan, Xin Zhang, Yu Han, Hai-Yan Nan, Yu-Chuan Hu, Bo Hu, Song-Lin Yan, Jin Zhang, Dong-Liang Cheng, et al. "Glioma grading on conventional MR images: A deep learning study with transfer learning," Frontiers in Neuroscience, Vol. 12, 804, 2018.
18. Selvapandian, A. and K. Manivannan, "Fusion based glioma brain tumor detection and segmentation using ANFIS classification," Computer Methods and Programs in Biomedicine, Vol. 166, 33-38, 2018.
19. Anaraki, Amin Kabir, Moosa Ayati, and Foad Kazemi, "Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms," Biocybernetics and Biomedical Engineering, Vol. 39, No. 1, 63-74, 2019.
20. Ghanem, Y., "Brain tumor detection dataset," [Online] Available: https://universe.roboflow.com/yousef-ghanem-jzj4y/brain-tumor-detection-fpf1f, Jul. 2022.
21. Qi, Yaolei, Yuting He, Xiaoming Qi, Yuan Zhang, and Guanyu Yang, "Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation," Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 6070-6079, 2023.
22. Ouyang, Daliang, Su He, Guozhong Zhang, Mingzhu Luo, Huaiyong Guo, Jian Zhan, and Zhijie Huang, "Efficient multi-scale attention module with cross-spatial learning," ICASSP 2023 --- 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1-5, Rhodes Island, Greece, Jun. 2023.
23. Wang, Jiaqi, Kai Chen, Rui Xu, Ziwei Liu, Chen Change Loy, and Dahua Lin, "Carafe: Content-aware reassembly of features," Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 3007-3016, Seoul, Korea (South), Oct. 2019.
24. Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7464-7475, Vancouver, BC, Canada, Jun. 2023.
25. Wang, Chien-Yao, I.-Hau Yeh, and Hong-Yuan Mark Liao, "YOLOv9: Learning what you want to learn using programmable gradient information," ArXiv Preprint ArXiv:2402.13616, 2024.
