本文關(guān)鍵詞：HEp-2樣本圖片陰陽(yáng)性分類算法研究　出處：《深圳大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： HEp-2 陰陽(yáng)性分類 全局特征 局部特征 深度學(xué)習(xí)

【摘要】：人體表皮細(xì)胞(HEp-2)在醫(yī)學(xué)疾病檢測(cè)上有著重要作用,其樣本圖片的診斷一般由專業(yè)人員通過(guò)肉眼觀察完成,工作強(qiáng)度大且容易受主觀因素影響。近年來(lái)人們嘗試用計(jì)算機(jī)視覺(jué)算法來(lái)進(jìn)行HEp-2樣本自動(dòng)化判讀。該過(guò)程分為兩個(gè)部分,第一部分為陰陽(yáng)性判斷,第二部分是樣本的核型判斷。目前業(yè)界研究主要集中在第二部分,第一部分的工作還較少。針對(duì)該問(wèn)題,本文采用了兩種不同的方法來(lái)解決HEp-2樣本圖片陰陽(yáng)性判斷問(wèn)題。第一種方法是結(jié)合全局特征與局部特征來(lái)對(duì)HEp-2樣本圖片進(jìn)行陰陽(yáng)性分類,首先根據(jù)樣本的全局特征判別出那些較為明顯的樣本,剩下的樣本通過(guò)一系列的預(yù)處理,圖像增強(qiáng),物體分割等方法確定感興趣區(qū)域,再在這些區(qū)域上提取局部特征并對(duì)樣本進(jìn)行分類。第二種方法是用深度學(xué)習(xí)技術(shù)來(lái)解決該問(wèn)題,本文首先以旋轉(zhuǎn)、尺度變換等方法對(duì)訓(xùn)練數(shù)據(jù)進(jìn)行擴(kuò)充,再選取VGG-16和Goog LeNet這兩種網(wǎng)絡(luò)進(jìn)行訓(xùn)練,將訓(xùn)練好的網(wǎng)絡(luò)用于HEp-2樣本圖片分類。最后,本文還嘗試將深度學(xué)習(xí)方法和SVM(支持向量機(jī))結(jié)合起來(lái),用卷積神經(jīng)網(wǎng)絡(luò)提取特征,SVM進(jìn)行分類,發(fā)揮二者的優(yōu)勢(shì)共同解決問(wèn)題。本文在含877張陽(yáng)性樣本,413張陰性樣本的SZU數(shù)據(jù)庫(kù)上進(jìn)行測(cè)試,測(cè)試結(jié)果表明深度學(xué)習(xí)方法在整體結(jié)果上比結(jié)合全局特征和局部特征的方法更好,其總體準(zhǔn)確率最高能達(dá)到99.87%。論文最后介紹了HEp-2樣本圖片陰陽(yáng)性分類系統(tǒng)的軟件開(kāi)發(fā)過(guò)程和運(yùn)行效果。
[Abstract]:Human epidermal cells (HEp-2) play an important role in the detection of medical diseases, and the diagnosis of the sample images is generally completed by the professionals through the naked eye observation. In recent years, people try to use computer vision algorithm to interpret HEp-2 samples automatically. The process is divided into two parts, the first part is the yin-yang judgment. The second part is the karyotype judgment of the sample. At present, the industry research is mainly focused on the second part, the first part of the work is still less. In this paper, two different methods are used to solve the problem of judging the Yin and Yang of HEp-2 samples. The first method is to classify the images of HEp-2 samples by combining global and local features. First of all, according to the global characteristics of the samples to identify the more obvious samples, the rest of the samples through a series of preprocessing, image enhancement, object segmentation and other methods to determine the region of interest. Then the local features are extracted from these regions and the samples are classified. The second method is to solve the problem by depth learning. Firstly, the training data are expanded by rotation, scale transformation and so on. Then select VGG-16 and Goog LeNet for training, and use the trained network for HEp-2 sample image classification. Finally. This paper also attempts to combine the depth learning method with SVM (support vector machine), and use convolution neural network to extract features and SVM for classification. Taking advantage of the two methods to solve the problem together. This paper was tested on the SZU database with 877 positive samples and 413 negative samples. The test results show that the depth learning method is better than the method combining global and local features in the overall results. The overall accuracy of the system can reach 99.87. Finally, the software development process and running effect of the HEp-2 sample image Yin and Yang classification system are introduced.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R446;TP391.41

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