發(fā)布時間：2018-06-27 05:13

  本文選題：卷積神經網(wǎng)絡 + 醫(yī)學圖像處理　； 參考：《湖北工業(yè)大學》2017年碩士論文

【摘要】：卷積神經網(wǎng)絡對圖像分類問題的處理往往優(yōu)于其他同類型算法,其中的卷積層和子采樣層具有能夠提取樣本特征的功能,而共享權值的特點又極大減少了網(wǎng)絡需要訓練的參數(shù)�？萍疾粩噙M步的今天,醫(yī)療技術也得到了飛快的發(fā)展,從中產生的各種病癥檢查圖片更是數(shù)不勝數(shù)。醫(yī)師急需擺脫各種繁重的醫(yī)學圖像篩查工作,且如何從無數(shù)的病例圖中找出某種疾病的相似特征等;如此種種困難不斷激勵著研究人員,醫(yī)學圖像的研究也漸漸的成為了熱點。本文對卷積神經網(wǎng)絡應用于兩類醫(yī)學圖像展開了研究,其中一類為能反映身體疾病的眼球血絲圖,另一類為含有各種級數(shù)的腦膠質瘤核磁共振成像圖。全文工作如下:(1)首先介紹了卷積神經網(wǎng)絡的發(fā)展歷程,包括國外與國內對其研究的成果,并且詳細的說明了卷積神經網(wǎng)絡的結構,算法以及推導,完整的闡述了復雜的圖像分類問題中應用卷積神經網(wǎng)絡的優(yōu)越性。(2)在經典LeNet-5卷積神經網(wǎng)絡結構上實施改進,設計了具有不同卷積核,不同子采樣方式與不同分類器的網(wǎng)絡結構,并把此結構用于解決識別眼球血絲病癥問題。同時在實驗環(huán)節(jié)對輸入層樣本尺寸,網(wǎng)絡的迭代次數(shù)進行了探究,對比了改進結構與LeNet-5在使用同一樣本數(shù)據(jù)集情況下的區(qū)別,實驗表明改進結構能很好的分類眼球血絲所反映的病癥。(3)根據(jù)腦膠質瘤多層圖片的特點,并基于眼球血絲網(wǎng)絡模型,設計出多列卷積神經網(wǎng)絡結構:每一層的腦膠質瘤樣本作為每一列的輸入,同時增加了卷積和子采樣層的層數(shù),并使用Maxout激活函數(shù)替代了傳統(tǒng)神經網(wǎng)絡中經常使用的Sigmoid函數(shù)。實驗部分取多列結構與單列結構,人工提取特征方式實施了對比,結果凸顯了多列結構在膠質瘤分級上的優(yōu)勢;此外還對樣本進行優(yōu)化處理,進一步提高了分級準確度。最后本文對多列卷積神經網(wǎng)絡計算進行了可視化處理,從視覺方面解釋了每層的工作過程。
[Abstract]:Convolution neural network is usually superior to other similar algorithms in image classification problem. The convolution layer and sub-sampling layer can extract the feature of samples, and the characteristics of shared weights greatly reduce the network parameters that need to be trained. With the development of science and technology, medical technology is developing rapidly. Doctors urgently need to get rid of all kinds of heavy medical image screening work, and how to find out the similar characteristics of a disease from countless case maps, and so on; such difficulties continue to inspire researchers, medical image research has gradually become a hot spot. In this paper, the application of convolutional neural networks to two kinds of medical images is studied. One is a hemodigram of the eyeball which can reflect the body disease, the other is a magnetic resonance imaging of glioma with various stages. The main work of this paper is as follows: (1) the development of convolutional neural network is introduced, including the research results both at home and abroad, and the structure, algorithm and derivation of convolutional neural network are explained in detail. The advantages of applying convolution neural network in complex image classification problem are discussed. (2) the network structure with different convolution kernel, different subsampling method and different classifier is designed by improving the classical LeNet-5 convolutional neural network structure. And this structure is used to solve the problem of identifying ocular blood disease. At the same time, the sample size of the input layer and the number of iterations of the network are explored in the experiment, and the difference between the improved structure and LeNet-5 in the case of using the same sample data set is compared. The experimental results show that the improved structure can well classify the diseases reflected by the blood filaments of the eyeball. (3) according to the characteristics of the multilayer images of gliomas and based on the network model of the blood filaments of the eyeball, The multi-column convolution neural network structure is designed: each layer of glioma samples is used as the input of each column and the number of layers of convolution and sub-sampling layers is increased. The Maxout activation function is used to replace the Sigmoid function which is often used in the traditional neural network. In the experiment, the multi-column structure and single-row structure are compared, and the results show the advantages of multi-column structure in glioma classification. In addition, the sample is optimized to further improve the classification accuracy. Finally, the multicolumn convolution neural network computation is visualized, and the working process of each layer is explained from the visual point of view.
【學位授予單位】：湖北工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP183

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