Deep Transfer Learning-Based COVID-19 Prediction Using Chest X-Rays

Al-Tawfiq, J. A., Assiri, A., Memish, Z. A. (2013). Middle East respiratory syndrome novel corona (MERS-CoV) infection. Saudi Medical Journal, 34(10), 991–994.
Google Scholar | Medline Cohen, J. P., Morrison, P., Dao, L. (2020). Covid–19 image data collection. arXiv preprint arXiv:2003.11597
Google Scholar Cui, Z., Yang, J., Qiao, Y. (2016). Brain MRI segmentation with patch-based CNN approach [Conference session]. 35th Chinese Control Conference (CCC), Chengdu, China. http://ieeecss.org/event/35th-chinese-control-conference
Google Scholar Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database [Conference session]. IEEE Conference on Computer Vision and Pattern Recognition, Miami, Florida, USA.
Google Scholar Gao, J., Tian, Z., Yang, X. (2020). Breakthrough: Chloroquine phosphate has shown apparent efficacy in treatment of COVID–19 associated pneumonia in clinical studies. BioScience Trends. https://doi.org/10.5582/bst.2020.01047
Google Scholar Gautret, P., Lagier, J. C., Parola, P., Hoang, V. T., Meddeb, L., Mailhe, M., Doudier, B., Courjon, J., Giordanengo, V., Vieira, V. E., Tissot Dupont, H., Honoré, S., Colson, P., Chabrière, E., La Scola, B., Rolain, J. M., Brouqui, P., Raoult, D. (2020). Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial. International journal of antimicrobial agents, 56(1), 105949. https://doi.org/10.1016/j.ijantimicag.2020.105949
Google Scholar Guarner, J. (2020). Three Emerging Coronaviruses in Two Decades: The Story of SARS, MERS, and Now COVID-19. American Journal of Clinical Pathology, 153(4), 420–421. doi:10.1093/ajcp/aqaa02.
Google Scholar | Crossref | Medline Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402–2410.
Google Scholar | Crossref | Medline | ISI Han, W., Qin, L., Bay, C., Chen, X., Yu, K.-H., Miskin, N., Li, A., Xu, X., Young, G. (2020). Deep transfer learning and radiomics feature prediction of survival of patients with high-grade gliomas. American Journal of Neuroradiology, 41(1), 40–48.
Google Scholar | Crossref | Medline He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition [Conference session]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf
Google Scholar Howard, J., Gugger, S. (2020). Fastai: A layered API for deep learning. Information, 11(2), 108.
Google Scholar | Crossref Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q. (2017). Densely connected convolutional networks [Conference session]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.pdf
Google Scholar Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. arXiv preprint arXiv:1602.07360.
Google Scholar Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y. (2017). The one hundred layers Tiramisu: Fully convolutional densenets for semantic segmentation [Conference session]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. https://openaccess.thecvf.com/content_cvpr_2017_workshops/w13/papers/Jegou_The_One_Hundred_CVPR_2017_paper.pdf
Google Scholar Kermany, D., Zhang, K., Goldbaum, M. (2018). Large dataset of labeled optical coherence tomography (OCT) and chest X-ray images. Mendeley Data. https://data.mendeley.com/datasets/rscbjbr9sj/3
Google Scholar Khan, S., Siddique, R., Ali, A., Xue, M., Nabi, G. (2020). Novel coronavirus, poor quarantine, and the risk of pandemic. Journal of Hospital Infection. https://pubmed.ncbi.nlm.nih.gov/32057788/
Google Scholar Kharitonov, S., Yates, D., Barnes, P. (1995). Increased nitric oxide in exhaled air of normal human subjects with upper respiratory tract infections. European Respiratory Journal, 8(2), 295–297.
Google Scholar | Crossref | Medline Khened, M., Kollerathu, V. A., Krishnamurthi, G. (2019). Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Medical image analysis, 51, 21–45.
Google Scholar | Crossref | Medline Liu, C., Cao, Y., Alcantara, M., Liu, B., Brunette, M., Peinado, J., Curioso, W. (2017). TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network [Conference session]. 2017 IEEE International Conference on Image Processing (ICIP). https://www.cs.uml.edu/∼cliu/pub/ICIP_2017_CameraReady_preprint.pdf
Google Scholar Lundervold, A. S., Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik, 29(2), 102–127.
Google Scholar | Crossref | Medline Narin, A., Kaya, C., Pamuk, Z. (2020). Automatic detection of coronavirus disease (covid–19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849.
Google Scholar Ng, M.-Y., Lee, E. Y., Yang, J., Yang, F., Li, X., Wang, H., Lui, M. M.-S., Lo, C. S.-Y., Leung, B., Khong, P.-L. (2020). Imaging profile of the COVID–19 infection: Radiologic findings and literature review. Radiology: Cardiothoracic Imaging, 2(1), e200034.
Google Scholar | Crossref | Medline Powers, D. (2008). Evaluation: From precision, recall and f-factor to roc, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2, 2229–3981.
Google Scholar Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225.
Google Scholar Razzak, M. I., Naz, S., Zaib, A. (2018). Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps (pp. 323–350). Springer.
Google Scholar | Crossref Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252.
Google Scholar | Crossref | ISI Sethy, P. K., Behera, S. K. (2020). Detection of coronavirus disease (COVID–19) based on deep features. Preprints. https://www.preprints.org/manuscript/202003.0300/v1
Google Scholar Smith, L. N. (2017). Cyclical learning rates for training neural networks. Paper presented at the 2017 IEEE winter conference on applications of computer vision (WACV). doi: 10.1109/WACV.2017.58
Google Scholar Srivastava, R. K., Greff, K., Schmidhuber, J. (2015). Training very deep networks. Advances in Neural Information Processing Systems. https://proceedings.neurips.cc/paper/2015/file/215a71a12769b056c3c32e7299f1c5ed-Paper.pdf
Google Scholar Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C. (2018). A survey on deep transfer learning [Conference session]. International Conference on Artificial Neural Networks. https://arxiv.org/pdf/1808.01974.pdf
Google Scholar Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X. (2020). A deep learning algorithm using CT images to screen for Corona virus disease (COVID–19). medRxiv. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7904034/
Google Scholar Xu, X., Lin, J., Tao, Y., Wang, X. (2018, November). An improved DenseNet method based on transfer learning for fundus medical images. In 2018 7th International Conference on Digital Home (ICDH) (pp. 137-140). IEEE.
Google Scholar Yan, L., Zhang, H.-T., Xiao, Y., Wang, M., Sun, C., Liang, J., Li, S., Zhang, M., Guo, Y., Xiao, Y. (2020). Prediction of survival for severe COVID–19 patients with three clinical features: Development of a machine learning-based prognostic model with clinical data in Wuhan. medRxiv. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Yan%2C+L.%2C+Zhang%2C+H.-T.%2C+Xiao%2C+Y.%2C+Wang%2C+M.%2C+Sun%2C+C.%2C+Liang%2C+J.%2C+Li%2C+S.%2C+Zhang%2C+M.%2C+Guo%2C+Y.%2C+%26+Xiao%2C+Y.+%282020%29.+Prediction+of+survival+for+severe+COVID%E2%80%9319+patients+with+three+clinical+features%3A&btnG=
Google Scholar Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Wang, X. (2020). Deep learning-based detection for COVID–19 from chest CT using weak label. medRxiv. https://doi.org/10.1101/2020.03.12.20027185
Google Scholar

留言 (0)

沒有登入
gif