本文關鍵詞： 乳腺癌 乳腺X線圖像 計算機輔助診斷 卷積神經(jīng)網(wǎng)絡 遷移學習　出處：《昆明理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：乳腺癌是女性最常見的惡性腫瘤之一,女性一生中患乳腺癌的可能性約為10%,在所有女性惡性腫瘤中,乳腺癌的發(fā)病率已居于首位,同時其致死率高達40%以上。目前對乳腺癌尚無積極的預防手段,早期診斷和及時治療是提高乳腺癌術后生存率的唯一途徑。但是乳腺腫塊的研究仍存在一定的難點,因為乳腺腫塊大小和對比度差異性較大,而且易受偽影和周圍腺體組織的干擾,極大地影響診斷系統(tǒng)的精度。本文在對目前乳腺癌計算機輔助診斷系統(tǒng)研究的基礎上,深入研究了卷積神經(jīng)網(wǎng)絡模型在乳腺腫塊分類中的應用問題,開展的主要研究工作如下:(1)乳腺腫塊圖像去噪。為了盡可能消除噪聲對乳腺X線圖像的影響,同時又保證乳腺腫塊的邊緣信息,本文分析了乳腺X線影像中噪聲的特點,對比多種圖像去噪算法,并在乳腺X線影像上進行試驗,經(jīng)實驗結果確定使用小波算法去除圖像噪聲。(2)腫塊區(qū)域分割與形態(tài)學處理。論文在乳腺腫塊的分割方面,應用大津算法對乳腺X線影像進行分割,提取出感興趣區(qū)域。然后運用形態(tài)學方法對粗分割得來的感興趣區(qū)域進行膨脹與開閉運算處理,用于保存腫塊圖像的邊緣信息和消除腫塊內部的孔洞,得到最終的腫塊圖像。(3)腫塊分類。論文選擇使用遷移學習的方法,將大規(guī)模深度卷積神經(jīng)網(wǎng)絡應用在乳腺腫塊良惡性區(qū)分中。論文對卷積神經(jīng)網(wǎng)絡模型結構和訓練過程進行分析與研究,嘗試使用遷移學習的方法,將在自然圖像集上訓練完畢的GoogLeNet和AlexNet在乳腺腫塊圖像上進行微調,微調后的模型應用在乳腺腫塊的分類中,實現(xiàn)了基于卷積神經(jīng)網(wǎng)絡的乳腺腫塊良惡性的區(qū)分。文中還對比從乳腺圖像上直接訓練的淺層卷積神經(jīng)網(wǎng)絡和人工設定特征實現(xiàn)分類的幾種模型,實驗結果也表明了基于遷移學習的深度卷積神經(jīng)網(wǎng)絡模型在乳腺腫塊良惡性區(qū)分中存在較大的優(yōu)勢。
[Abstract]:Breast cancer is one of the most common malignant tumors in women. At the same time, the fatality rate of breast cancer is over 40%. At present, there is no positive preventive method for breast cancer. Early diagnosis and timely treatment are the only way to improve the survival rate of breast cancer. However, there are still some difficulties in the study of breast masses. Because the size and contrast of breast masses vary greatly, and they are easily disturbed by artifacts and surrounding glands, the accuracy of the diagnostic system is greatly affected. In this paper, the application of convolution neural network model in classification of breast masses is deeply studied. The main research work is as follows: 1) De-noising of breast masses. At the same time, the edge information of breast mass is guaranteed. This paper analyzes the characteristics of noise in mammography, compares various image denoising algorithms, and carries out experiments on mammography. The experimental results confirm that the wavelet algorithm is used to remove the noise of the image and to deal with the morphology of the mass. In the aspect of the segmentation of the breast mass, the paper applies the Otsu algorithm to segment the mammary mammary X-ray image. The region of interest is extracted, and then the rough segmentation of the region of interest is processed by the morphological method, which is used to preserve the edge information of the mass image and eliminate the holes inside the mass. The final mass image is obtained. The large scale deep convolution neural network is applied to distinguish benign and malignant breast masses. This paper analyzes and studies the model structure and training process of convolution neural network, and tries to use the transfer learning method. The GoogLeNet and AlexNet trained on the natural image set are fine-tuned on the breast mass image, and the fine-tuned model is applied to the classification of the breast mass. The classification of benign and malignant breast masses based on convolution neural network is realized. The experimental results also show that the deep convolution neural network model based on migration learning has a great advantage in differentiating benign and malignant breast masses.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R737.9;TP391.41

【參考文獻】

相關期刊論文 前4條

1 江帆;劉輝;王彬;孫曉峰;;基于火焰圖像CNN的轉爐煉鋼吹煉終點判斷方法[J];計算機工程;2016年10期

2 王志明;;無參考圖像質量評價綜述[J];自動化學報;2015年06期

3 莊福振;羅平;何清;史忠植;;遷移學習研究進展[J];軟件學報;2015年01期

4 方志沂,劉君;乳腺癌診斷進展[J];中國腫瘤臨床;2002年11期

相關碩士學位論文 前1條

1 張橋新;乳腺腫塊計算機輔助檢測算法研究[D];西安電子科技大學;2009年

，

本文編號：1497097