本文關鍵詞： 宮頸細胞病變識別 卷積神經(jīng)網(wǎng)絡 網(wǎng)絡分類識別性能 樣本擴容 BN算法　出處：《廣西師范大學》2017年碩士論文　論文類型：學位論文

【摘要】：目前傳統(tǒng)的宮頸細胞識別主要都是先經(jīng)過細胞圖像分割,人工設計算子選取特征,然后選用分類器進行識別。在宮頸細胞分割與特征提取階段,使用此類方法需要掌握一定的病理醫(yī)學常識,而且由于特征是人為選取,有時候選取的特征并不具有代表性,會導致識別效果不明顯,因此本文將深度學習框架下的卷積神經(jīng)網(wǎng)絡應用于宮頸細胞識別的領域進行研究。卷積神經(jīng)網(wǎng)絡是將人工神經(jīng)網(wǎng)絡和深度學習相結合的一種新型人工神經(jīng)網(wǎng)絡,能夠將特征提取與識別分類工作相結合,其最主要的特點是局部感受野、權值共享和空間子采樣,能夠提取數(shù)據(jù)的局部特征,因此在圖像識別領域獲得了廣泛的應用。本文將卷積神經(jīng)網(wǎng)絡模型應用到宮頸細胞圖像識別中,本文的方案具有圖像可以直接輸入,特征自主提取的特點,可以提高宮頸細胞圖像識別的智能化水平與效率。本文完成的主要研究工作如下:(1)本文詳細闡述了卷積神經(jīng)網(wǎng)絡的理論、特點和結構,為模型的改進提供理論基礎。本文在LeNet-5模型的基礎上,構造了若干個具有不同的層間連接方式的抽取特征的濾波器層的卷積神網(wǎng)絡模型,并將這些模型應用到宮頸細胞圖像的識別中,通過仿真實驗比較各個模型的分類效果,分析了不同數(shù)量的過濾器對網(wǎng)絡性能的影響。(2)在上文研究的基礎上繼續(xù)探究影響網(wǎng)絡識別性能的因素,通過調整卷積神經(jīng)網(wǎng)絡的卷積核尺寸、下采樣方法、激活函數(shù)以及擴增圖像數(shù)據(jù)集來進行對比仿真實驗。仿真結果表明,合理的參數(shù)及方法選擇都會提高網(wǎng)絡的分類識別性能,尤其是增加圖像數(shù)據(jù)集對網(wǎng)絡性能提升效果明顯。(3)經(jīng)過分析了卷積神經(jīng)網(wǎng)絡識別分類性能的影響因素之后,總結了合理選擇參數(shù)以及方法的規(guī)律,構造了一個宮頸細胞圖像分類識別性能最佳的網(wǎng)絡結構。本文構造了一個增加卷積層過濾器數(shù)量的網(wǎng)絡,并加入了 BN算法作為BN層,BN算法能夠加快網(wǎng)絡訓練速度與網(wǎng)絡收斂速度,然后加入dropout方法,隨機抑制網(wǎng)絡中的神經(jīng)元,最后使用softmax作為分類器,對宮頸細胞進行病變分類識別。仿真實驗結果表明:本文構建的改進卷積神經(jīng)網(wǎng)絡對宮頸細胞圖像二分類識別率達到98.36%,識別效果優(yōu)于ANN方法、SVM方法、KNN方法、貝葉斯方法和線性判別方法等多種方法,識別率比傳統(tǒng)貝葉斯方法提高了 12.21%,比人工神經(jīng)網(wǎng)絡方法(ANN)提高了5.65%,具有一定的實用價值。
[Abstract]:At present, the traditional cervical cell recognition is mainly through cell image segmentation, artificial design operator to select features, and then select classifier for recognition. In the cervical cell segmentation and feature extraction stage. The use of this method requires a certain degree of common sense of pathology medicine, and because the feature is artificial selection, sometimes the selected features are not representative, which will lead to the recognition effect is not obvious. In this paper, the convolution neural network in the framework of deep learning is applied to the field of cervical cell recognition. Convolution neural network is a new type of artificial neural network which combines artificial neural network and depth learning. Feature extraction and classification can be combined, the most important characteristics of the local receptive field, weight sharing and spatial subsampling, can extract the local features of the data. Therefore, it has been widely used in the field of image recognition. In this paper, the convolution neural network model is applied to cervical cell image recognition. It can improve the intelligent level and efficiency of cervical cell image recognition. The main research work accomplished in this paper is as follows: 1) the theory, characteristics and structure of convolution neural network are described in detail in this paper. On the basis of LeNet-5 model, this paper constructs several convolutional network models of filter layer with different interlayer connection characteristics. These models are applied to the recognition of cervical cell images, and the classification effects of each model are compared by simulation experiments. This paper analyzes the influence of different number of filters on network performance. (2) on the basis of the above research, we continue to explore the factors that affect the network identification performance, and adjust the convolution kernel size of the convolution neural network. The simulation results show that reasonable selection of parameters and methods can improve the classification and recognition performance of the network. In particular, adding image data sets to improve the network performance is obvious. After analyzing the factors affecting the classification performance of convolution neural networks, the reasonable selection of parameters and the rules of the method are summarized. A network structure with the best performance of classification and recognition of cervical cell images is constructed. A network to increase the number of convolutional layer filters is constructed and BN algorithm is added as the BN layer. BN algorithm can speed up the network training speed and network convergence speed, then add dropout method, randomly suppress the neurons in the network, and finally use softmax as classifier. The result of simulation experiment shows that the improved convolution neural network is better than ANN method in the recognition rate of cervical cell image. The recognition rate of SVM method is 12.21% higher than that of traditional Bayesian method, such as Bayesian method, Bayesian method and linear discriminant method. Compared with the artificial neural network (Ann) method, it is 5.65% higher than the artificial neural network (Ann) method, and has certain practical value.
【學位授予單位】：廣西師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R737.33;TP391.41;TP183

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