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

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题名:	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
贡献者:	蔡炎龍張淑媛王庭恩
关键词:	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
上传时间:	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.
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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
数据类型:	thesis
显示于类别:	[應用數學系] 學位論文

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