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  本文關(guān)鍵詞：HEp-2樣本圖片陰陽性分類算法研究　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 陰陽性分類 全局特征 局部特征 深度學(xué)習(xí)

【摘要】：人體表皮細胞(HEp-2)在醫(yī)學(xué)疾病檢測上有著重要作用,其樣本圖片的診斷一般由專業(yè)人員通過肉眼觀察完成,工作強度大且容易受主觀因素影響。近年來人們嘗試用計算機視覺算法來進行HEp-2樣本自動化判讀。該過程分為兩個部分,第一部分為陰陽性判斷,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。針對該問題,本文采用了兩種不同的方法來解決HEp-2樣本圖片陰陽性判斷問題。第一種方法是結(jié)合全局特征與局部特征來對HEp-2樣本圖片進行陰陽性分類,首先根據(jù)樣本的全局特征判別出那些較為明顯的樣本,剩下的樣本通過一系列的預(yù)處理,圖像增強,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樣本進行分類。第二種方法是用深度學(xué)習(xí)技術(shù)來解決該問題,本文首先以旋轉(zhuǎn)、尺度變換等方法對訓(xùn)練數(shù)據(jù)進行擴充,再選取VGG-16和Goog LeNet這兩種網(wǎng)絡(luò)進行訓(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)結(jié)合起來,用卷積神經(jīng)網(wǎng)絡(luò)提取特征,SVM進行分類,發(fā)揮二者的優(yōu)勢共同解決問題。本文在含877張陽性樣本,413張陰性樣本的SZU數(shù)據(jù)庫上進行測試,測試結(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,其總體準確率最高能達到99.87%。論文最后介紹了HEp-2樣本圖片陰陽性分類系統(tǒng)的軟件開發(fā)過程和運行效果。
[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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R446;TP391.41

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