Open Access
| Issue |
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
|
|
|---|---|---|
| Article Number | 04006 | |
| Number of page(s) | 8 | |
| Section | Language & Image Processing | |
| DOI | https://doi.org/10.1051/itmconf/20235604006 | |
| Published online | 09 August 2023 | |
- V. Collins, Brain tumors: Classification and genes. Journal of Neurology, Neurosurgery & Psychiatry. 2004; 75(Suppl 2):ii2-ii11. [CrossRef] [Google Scholar]
- S. Hussain, S.M. Anwar, M. Majid, Segmentation of glioma tumors in brain using deep convolutional neural network, Neurocomputing 282 (2018) 248-261. [CrossRef] [Google Scholar]
- Robert, Christian., Machine learning, a probabilistic perspective. 2014: 62–63. [Google Scholar]
- T. Batchelor, Patient information: high-grade glioma in adults (Beyond the Basics), UpToDate (2013) 1-6. [Google Scholar]
- H. Ohgaki, P. Kleihues, Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas, Journal of Neuropathology & Ex513 perimental, Neurology 64 (6) (2005) 479-489. [CrossRef] [Google Scholar]
- D.N.H. Louis, O. Ohgaki, D. Wiestler, W.K. Cavenee, WHO classification of tumors of the central nervous system, World Health Organization classification of tumors, World Health Organization. 2007. [Google Scholar]
- Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. TPAMI, 2017. [Google Scholar]
- Mazzara, G.P., Velthuizen, R.P., Pearlman, J.L., Greenberg, H.M., Wagner, H.: Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int. J. Radiat. Oncol. Biol. Phys. 59, 300-12 (2004). [CrossRef] [Google Scholar]
- Jones, T.L., Byrnes, T.J., Yang, G., Howe, F.A., Bell, B.A., Barrick, T.R.: Brain tumor classification using the diffusion tensor image segmentation (DSEG) technique. Neuro. Oncol. 17, 466-476 (2014). [Google Scholar]
- Wu, W., Chen, A.Y.C., Zhao, L., Corso, J.J.: Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixelpairwise affinity and superpixel-level features. Int. J. Comput. Assist. Radiol. Surg. (2013). [Google Scholar]
- Gotz, M., Weber, C., Blocher, J., Stieltjes, B., Meinzer, H., Maier-Hein, K.: Extremely randomized trees based brain tumor segmentation. In: Proceeding of BRATS Challenge-MICCAI (2014). [Google Scholar]
- Soltaninejad, M., Yang, G., Lambrou, T., Allinson, N., Jones, T.L., Barrick, T.R., Howe, F.A., Ye, X.: Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int. J. Comput. Assist. Radiol. Surg. (2016) [Google Scholar]
- Jafari, M., Kasaei, S.: Automatic brain tissue detection in MRI images using seeded region growing segmentation and neural network classification. Aust. J. Basic Appl. Sci. 5, 1066-1079 (2011). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.