Open Access
Issue |
SHS Web Conf.
Volume 139, 2022
The 4th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2022)
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Article Number | 03005 | |
Number of page(s) | 8 | |
Section | Topics in Computer Science | |
DOI | https://doi.org/10.1051/shsconf/202213903005 | |
Published online | 13 May 2022 |
- R. Han, L. Huang, H. Jiang, J. Dong, H. Peng, and D. Zhang, “Early clinical and ct manifestations of coronavirus disease 2019 (covid-19) pneumonia,” American Journal of Roentgenology 215, 338–343 (2020). [CrossRef] [Google Scholar]
- W. H. Organization, “Who coronavirus disease (covid-19) dashboard,” (2020). [Google Scholar]
- Y. Ji, Z. Ma, M. P. Peppelenbosch, and Q. Pan, “Potential association between COVID-19 mortality and health-care resource availability,” The Lancet Global Health 8, e480 (2020). [CrossRef] [Google Scholar]
- B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak,, and the CAMELYON16 Consortium, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318, 2199–2210 (2017). [CrossRef] [Google Scholar]
- P. Lakhani and B. Sundaram, “Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks,” Radiology 284, 574–582 (2017). [CrossRef] [Google Scholar]
- A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115–118 (2017). [CrossRef] [Google Scholar]
- M. Nakamura, J. Wang, S. Phea, and A. B. Abdallah, “Comprehensive study of coronavirus disease 2019 (covid-19) classification based on deep convolution neural networks,” in SHS Web of Conferences, Vol. 102 (EDP Sciences, 2021) p. 04007. [CrossRef] [EDP Sciences] [Google Scholar]
- J. Wang, Y. Bao, Y. Wen, H. Lu, H. Luo, Y. Xiang, X. Li, C. Liu, and D. Qian, “Prior-attention residual learning for more discriminative covid-19 screening in ct images,” IEEE Transactions on Medical Imaging 39, 2572–2583 (2020). [CrossRef] [Google Scholar]
- X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, W. Liu, and C. Zheng, “A weakly-supervised framework for covid-19 classification and lesion localization from chest ct,” IEEE Transactions on Medical Imaging 39, 2615–2625 (2020). [CrossRef] [Google Scholar]
- L. Meng, D. Dong, L. Li, M. Niu, Y. Bai, M. Wang, X. Qiu, Y. Zha, and J. Tian, “A deep learning prognosis model help alert for covid-19 patients at high-risk of death: A multi-center study,” IEEE Journal of Biomedical and Health Informatics 24, 3576–3584 (2020). [CrossRef] [Google Scholar]
- A. B. Abdallah, H. Huang, N. K. Dang, and J. Song, “Ai processor,” (Japanese Patent Application Laid-Open No 2020-194733 Nov.2020). [Google Scholar]
- M. Nakamura, “Ai-enabled hardware-software system for pneumonia detection.” (MS thesis, The University of Aizu, Japan, March 2022.). [Google Scholar]
- P. Mooney, “Chest x-ray images (pneumonia),” (2020). [Google Scholar]
- T. H. Vu, R. Murakami, Y. Okuyama, and A. Ben Abdallah, “Efficient optimization and hardware acceleration of cnns towards the design of a scalable neuro inspired architecture in hardware,” in 2018 IEEE International Conference on Big Data and Smart Computing (BigComp) (2018) pp. 326–332. [CrossRef] [Google Scholar]
- L. Lu, Y. Liang, Q. Xiao, and S. Yan, “Evaluating fast algorithms for convolutional neural networks on fpgas,” in 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (IEEE, 2017) pp. 101–108. [CrossRef] [Google Scholar]
- H. Isihara, in An Introduction to FPGAs for Software Engineers (2017) pp. 96–165. [Google Scholar]
- J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, “Covid-19 image data collection: Prospective predictions are the future,” (2020), arXiv:2006.11988 [q-bio.QM]. [Google Scholar]
- J. Wang, M. Nakamura, and A. Ben Abdallah, “Efficient AI-Enabled pneumonia detection in chest x-ray images,” in 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech) (IEEE LifeTech 2022) (Osaka, Japan, 2022). [Google Scholar]
- O. Yuuki, J. Wang, O. M. Ikechukwu, and A. B. Abdallah, “Hardware acceleration of convolution neural network for ai-enabled realtime biomedical system,” in SHS Web of Conferences, Vol. 102 (EDP Sciences, 2021) p. 04019. [CrossRef] [EDP Sciences] [Google Scholar]
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