Open Access
ITM Web Conf.
Volume 45, 2022
2021 3rd International Conference on Computer Science Communication and Network Security (CSCNS2021)
Article Number 01037
Number of page(s) 8
Section Computer Technology and System Design
Published online 19 May 2022
  1. V. L. Feigin, B. A. Stark, C. O. Johnson, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. The Lancet Neurology, 20(10):795–820 (2021). [CrossRef] [Google Scholar]
  2. H. Abdulkader, E. F. Georges, S. Hadi, U. O. Dilber. Deep networks in identifying CT brain hemorrhage. Journal of Intelligent & Fuzzy Systems, 35(2):2215–2228 (2018). [CrossRef] [Google Scholar]
  3. A. D. Muhammad, Y. Kamil, O. Huseyin. Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning. Computational Intelligence and Neuroscience, 2019:1-12. [CrossRef] [Google Scholar]
  4. A. Motahareh, A. Ali, E. Mehdi. Brain tumor image segmentation via asymmetric/symmetric UNet based on two-pathway-residual blocks. Biomedical Signal Processing and Control, 69:102841 (2021). [CrossRef] [Google Scholar]
  5. L. Li, M. Wei, B. Liu, K. Atchaneeyasakul, F. Zhou, Z. Pan, S. Kumar, J. Zhang, Y. Pu, D. Liebeskind, F. Scalzo. Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images. IEEE journal of biomedical and health informatics, 25(5):1646-1659 (2021). [CrossRef] [Google Scholar]
  6. F. Liu, H. Jang, R. Kijowski, T. Bradshaw, A. B. McMillan. Deep Learning MR Imaging–based Attenuation Correction for PET/MR Imaging. Radiology, 286:2 (2017). [Google Scholar]
  7. K. He, X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016:770-778. [Google Scholar]
  8. O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science. 9351:234-241 (2015). [CrossRef] [Google Scholar]
  9. M. Hssayeni, M. Al-Janabi, A. D. Salman, H. F. Al-khafaji, Z. A. Yahya, B. Ghoraani. Intracranial Hemorrhage Segmentation Using A Deep Convolutional Model. Data, 5(1):14 (2020). [CrossRef] [Google Scholar]
  10. P. Chlap, M. Hang, N. Vandenberg, J. Dowling, L. Holloway, A. Haworth. A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology, 65:545-563 (2021). [CrossRef] [Google Scholar]
  11. A. Krizhevsky, I. Sutskever, G. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6):84-90 (2017). [Google Scholar]
  12. D. G. Shen, G. R. Wu, H. Suk. Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering, 19:221-248 (2017). [CrossRef] [Google Scholar]
  13. E. Kim, K. H. Lee, W. K. Sung. Optimizing Spatial Shift Point-Wise Quantization. IEEE Access, 9:68008-68016 (2021). [CrossRef] [Google Scholar]
  14. Z. S. Xu, M. M. Xia. Hesitant fuzzy entropy and cross-entropy and their use in multiattribute decision-making. International Journal of Intelligent Systems, 27(9):799822 (2012). [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.