政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/117818

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/117818

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113451/144438 (79%)
造訪人次 : 51260220 線上人數 : 795

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 理學院 > 應用數學系 > 學位論文 > Item 140.119/117818

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/117818

題名:	深度卷積神經網路辨認流感病毒細胞病變作用之應用 Recognizing Cytopathic Effects of Influenza Virus Using Deep Convolutional Neural Networks
作者:	王庭恩
貢獻者:	蔡炎龍張淑媛王庭恩
關鍵詞:	卷積神經網路流感病毒細胞病變作用 Convolutional neural network Influenza virus Cytopathic effects
日期:	2018
上傳時間:	2018-06-19 16:38:53 (UTC+8)
摘要:	流感病毒常年為國際間重要的流行病，檢驗方法因此也更為重要。在檢驗眾多方法中，細胞病變作用是個常用的檢驗病毒方式。一般來說，我們將細胞病變作用視為篩選測試，因此加快這個步驟的判別，可因此加快整個檢驗流程。且觀察細胞病變作用是個高度使用勞力的檢測方式，也需耗費許多時間觀察，所以我們利用卷積神經網路模型，讓機器可以自行判讀細胞影像。我們使用了 686 個樣本作為訓練資料，其中包含了 154 個陰性樣本和 532 個陽性樣本，在訓練結束後，機器對於訓練資料有 97.36% 的正確判讀率。爾後，我們採樣另外 400 個細胞樣本用來驗證其成效，其中 100 個為陰性，300 個為陽性。檢測樣本的判讀正確率高達 99.5%，其中的 300 個陽性皆成功識別，而陰性正確率為 97.99%。因此，我們可以期待之後可以利用此方式減少更多的人為判讀，讓檢測方式變得更有效率。 Observation of cytopathic effects by virus infection is a standard method to exam the presence of viruses. Viruses can infect specific cells and cause characteristic morphological changes. When we observe cytopathic effects, we can use the unique morphology change to classify virus species. The virus identifacation can be later confirmed to immunofluorescence staining. Con- sidering the screen test is essential but labor-intensive, we use deep learning to recognize the different patterns between normal cells and virus-infected cells. We took 154 10X normal cell photographs and 532 10X influenza virus infect- ing cell photographs to train the convolutional neural network model. The model we got is able to distinguish 97.36% of training data. Then we send 400 new photographs to the model. These photographs contain both normal cell photos and virus-infected cell photos. Our model can specifically identify 99.5% of the testing data. In particular, this model differentiate positive sam- ples accurately. The accuracy of positive samples reach up to 100%. On the other hand, the accuracy of negative controls is 97.99%. Hence, we expect to use this model to reduce the timing required for this labor-intensive screening test, and identify virus more specifically.
參考文獻:	[1] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine transla- tion by jointly learning to align and translate. CoRR, abs/1409.0473, 2014. [2] Carlson CA. Covalciuc KA, Webb KH. Comparison of four clinical specimen types for detection of influenza a and b viruses by optical immunoassay (flu oia test) and cell culture methods. J Clin Microbiol, 1999. [3] Kunihiko Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cyber- netics, 36:193–202, 1980. [4] McQuillin J. Gardner PS. Rapid virus diagnosis. application of immunofluorescence. 2d ed. London: Butterworths,, 1980. [5] Taber LH Piedra PA Clover RD Couch RB. Glezen WP, Keitel WA. Age distribution of patients with medically-attended illnesses caused by sequential variants of influenza a/h1n1: comparison to age-specific infection rates, 1978-1989. Am J Epidemiol, 1991. [6] Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feed- forward neural networks. In JMLR W&CP: Proceedings of the Thirteenth Interna- tional Conference on Artificial Intelligence and Statistics (AISTATS 2010), volume 9, pages 249–256, May 2010. [7] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In AISTATS, 2011. [8] J. B. Heaton, N. G. Polson, and J. H. Witte. Deep learning in finance. CoRR, abs/ 1602.06561, 2016. [9] D. Hubel and T. N. Wiesel. Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex. Journal of Physiology, 160:106–154, 1962. [10] Dennis Kasper; Anthony Fauci; Stephen Hauser; Dan Longo; J Jameson. Harrison’s Principles of Internal Medicine. New York : McGraw-Hill Education, 2015. [11] Hayden FG. Kaiser L, Briones MS. Performance of virus isolation and directigen flu a to detect influenza a virus in experimental human infection. J Clin Virol., December 1999. [12] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q. Weinberger, editors, NIPS, pages 1106–1114, 2012. [13] Ferguson D Landry ML. Simulfluor respiratory screen for rapid detection of multiple respiratory viruses in clinical specimens by immunofluorescence staining. J Clin Microbiol, February 2000. [14] S. Lawrence, C.L. Giles, Ah Chung Tsoi, and A.D. Back. Face recognition: a con- volutional neural-network approach. Neural Networks, IEEE Transactions on, 8(1): 98–113, January 1997. [15] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1:541–551, 1989. [16] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553): 436–444, may 2015. [17] Yann LeCun, Léon Bottou, Genevieve B. Orr, and Klaus-Robert Müller. Efficient backprop. In Neural Networks: Tricks of the Trade, This Book is an Outgrowth of a 1996 NIPS Workshop, pages 9–50, London, UK, UK, 1998. Springer-Verlag. [18] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, volume 86, pages 2278–2324, 1998. [19] Ch’ien Liu. Rapid diagnosis of human influenza infection from nasal smears by means of fluorescein-labeled antibody. Proc Soc Exp Biol Med, 92, 1956. [20] Junshui Ma, Robert P. Sheridan, Andy Liaw, George E. Dahl, and Vladimir Svet- nik. Deep neural nets as a method for quantitative structure–activity relationships. Journal of Chemical Information and Modeling, 55(2):263–274, feb 2015. [21] O’Donnell FT Atmar RL Greer J Demmler GJ. Noyola DE, Clark B. Comparison of a new neuraminidase detection assay with an enzyme immunoassay, immunofluo- rescence, and culture for rapid detection of influenza a and b viruses in nasal wash specimens. J Clin Microbiol, 2000. [22] Marc’Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, and Yann LeCun. Unsuper- vised learning of invariant feature hierarchies with applications to object recognition. In CVPR. IEEE Computer Society, 2007. [23] Faloona F Mullis KB Horn GT Erlich HA Arnheim N. Saiki RK, Scharf S. Enzy- matic amplification of beta-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia. Science., December 1985. [24] Tara N. Sainath, Abdel rahman Mohamed, Brian Kingsbury, and Bhuvana Ram- abhadran. Deep convolutional neural networks for lvcsr. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 2013. [25] Gregory A. Storch. Diagnostic virology. Clinical Infectious Diseases, 31:739–751, September 2000. [26] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In CVPR, pages 1–9. IEEE Computer Society, 2015. [27] Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf. Deepface: Closing the gap to human-level performance in face verification. In Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [28] John F. Enders Thomas H. Weller. Production of hemagglutinin by mumps and influenza a viruses in suspended cell tissue cultures. Proc Soc Exp Biol Med, 69, 1948. [29] Minjoon Kouh Charles Cadieu Ulf Knoblich Thomas Serre, Gabriel Kreiman and Tomaso Poggio. A quantitative theory of immediate visual recognition. Prog Brain Res, 26:1196, 2007. [30] Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, and Christoph Bregler. Efficient object localization using convolutional networks. CoRR, abs/1411.4280, 2014. [31] Jonathan J Tompson, Arjun Jain, Yann LeCun, and Christoph Bregler. Joint training of a convolutional network and a graphical model for human pose estimation. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 1799–1807. Curran Associates, Inc., 2014. [32] Shalaby H Murphy P Wall LV. Waner JL, Todd SJ. Comparison of directigen flu- a with viral isolation and direct immunofluorescence for the rapid detection and identification of influenza a virus. J Clin Microbiol, 1991. [33] H. Y. Xiong, B. Alipanahi, L. J. Lee, H. Bretschneider, D. Merico, R. K. C. Yuen, Y. Hua, S. Gueroussov, H. S. Najafabadi, T. R. Hughes, Q. Morris, Y. Barash, A. R. Krainer, N. Jojic, S. W. Scherer, B. J. Blencowe, and B. J. Frey. The human splicingcode reveals new insights into the genetic determinants of disease. Science, 347(6218): 1254806–1254806, dec 2014.
描述:	碩士國立政治大學應用數學系 104751008
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0104751008
資料類型:	thesis
顯示於類別:	[應用數學系] 學位論文

文件中的檔案:

沒有與此文件相關的檔案.

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋