MRI SEGMENTATION AND CLASSIFICATION OF BRAIN TUMORS USING NEURAL NETWORKS
Keywords:
Brain Tumors, Glioma, Meningoma, Pituitary Adenoma, MRI, Segmentation, Neural Networks, Image Processing, Mathematical ModelsAbstract
Algorithms and their mathematical models used for segmentation and classification of brain tumor MRI images are reviewed in this article. Algorithms used to identify different types of tumors such as glioma, meningoma and pituitary adenoma are analyzed. The results of segmentation and classification are expressed by mathematical equations, optimal solutions are shown at each stage.
References
1. Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., Erickson, B. J. "Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions." Journal of Digital Imaging, 2017.
2. He, K., Zhang, X., Ren, S., Sun, J. "Deep Residual Learning for Image Recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
3. Ronneberger, O., Fischer, P., Brox, T. "U-Net: Convolutional Networks for Biomedical Image Segmentation." In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015.
4. Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M. J. "Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations." In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2017.
5. Zhu, W., Huang, Y., Zeng, L., Chen, X., Liu, Y., Qiao, H. "Anatomynet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy." Medical Physics, 2019.
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