基于卷积神经网络的医学图像分类的研究
发布时间：2018-11-28 16:36
【摘要】：现代医院每天都会产出大量的医学图像,这些医学图像数据都会被传入医学云影像中心。由于云影像中心中的医学图像是杂乱无章的,所以在这些图像数据应用到实际的挖掘工作之前首先应对其进行清洗,得出适合挖掘的医学图像数据。随着数据挖掘概念的提出,很多优秀的数据挖掘方法由于其强大的分类能力也被应用到医学图像分类中,但是其中大部分都是先对其进行特征提取,即提取医学图像数据的统计学特征进而在得到的特征数据集上对其进行分析研究,利用一些比较好的统计学习方法进行分类。而近几年随着深度学习方法的研究取得重大的进展,一些较好的深度学习方法也自然而然的应用到医学图像分析领域,其中的典型代表就是卷积神经网络。利用卷积神经网络对图像进行分类不仅提高了图像分类的准确率,而且还可以省去传统统计学习方法特征工程部分,大大提高了图像分类的效率。因此本文主要对利用卷积神经网络对医学图像分类的方法以及利用卷积神经网络提取图像特征进行了研究。本文首先回顾了国内外在图像分类领域的研究现状,接下来介绍了传统的统计学习方法中应用在医学图像分类领域较为优越的词袋模型以及图像领域表征性较强的SIFT特征,并且详细介绍词袋模型的基础理论和应用领域以及SIFT的基础原理和应用。然后讲述了深度学习以及卷积神经网络的基本理论以及其在图像分类领域的应用。最后针对传统统计学习的分类方法和卷积神经网络方法各自的特点,进行了取其各自所长将两者结合起来的探索。在最后的通过实验结果进行验证部分,我们首先对利用卷积神经网络与利用词袋模型对医学图像分类的实验结果进行对比分析,说明基于深度学习方法的卷积神经网络在医学图像分析方面不仅可以省去人工特征工程的工作,而且分类效果比传统统计学习方法更好;然后通过将卷积神经网络自动提取特征以及传统分类方法的分类能力相结合进而对医学图像进行分类与前两种分类方法进行实验分析比较,验证了将基于深度学习的卷积神经网络与传统统计学习方法相结合的分类方法在医学图像分类领域较有很好的优越性。
[Abstract]:Modern hospitals produce a large number of medical images every day, which are passed into the medical cloud image center. Because the medical images in the cloud image center are chaotic, the medical image data suitable for mining should be cleaned first before they are applied to the actual mining work. With the development of the concept of data mining, many excellent data mining methods have been applied to medical image classification because of their powerful classification ability. That is to extract the statistical features of medical image data and then analyze them on the acquired feature data set and classify them by using some better statistical learning methods. In recent years, with the great progress in the research of deep learning methods, some better depth learning methods are naturally applied to the field of medical image analysis, the typical representative of which is convolution neural network. Using convolution neural network to classify images not only improves the accuracy of image classification but also saves the feature engineering of traditional statistical learning method and greatly improves the efficiency of image classification. In this paper, the methods of medical image classification using convolution neural network and image feature extraction by convolution neural network are studied in this paper. Firstly, this paper reviews the research status of image classification at home and abroad, then introduces the word bag model which is used in the field of medical image classification in traditional statistical learning methods and the SIFT feature with strong representativeness in image field. The basic theory and application field of word bag model and the basic principle and application of SIFT are introduced in detail. Then the basic theory of deep learning and convolution neural network and its application in image classification are described. Finally, according to the characteristics of the traditional statistical learning classification method and convolution neural network method, the author explores the combination of the two methods. In the last part, we compare and analyze the experimental results of medical image classification using convolution neural network and word bag model. It shows that the convolutional neural network based on the deep learning method can not only save the work of artificial feature engineering, but also has better classification effect than the traditional statistical learning method in medical image analysis. Then, by combining the automatic feature extraction of convolution neural network and the classification ability of traditional classification methods, the medical image classification is analyzed and compared with the first two classification methods. It is verified that the classification method which combines convolution neural network based on deep learning with traditional statistical learning method has better superiority in the field of medical image classification.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP183
本文编号：2363537
[Abstract]:Modern hospitals produce a large number of medical images every day, which are passed into the medical cloud image center. Because the medical images in the cloud image center are chaotic, the medical image data suitable for mining should be cleaned first before they are applied to the actual mining work. With the development of the concept of data mining, many excellent data mining methods have been applied to medical image classification because of their powerful classification ability. That is to extract the statistical features of medical image data and then analyze them on the acquired feature data set and classify them by using some better statistical learning methods. In recent years, with the great progress in the research of deep learning methods, some better depth learning methods are naturally applied to the field of medical image analysis, the typical representative of which is convolution neural network. Using convolution neural network to classify images not only improves the accuracy of image classification but also saves the feature engineering of traditional statistical learning method and greatly improves the efficiency of image classification. In this paper, the methods of medical image classification using convolution neural network and image feature extraction by convolution neural network are studied in this paper. Firstly, this paper reviews the research status of image classification at home and abroad, then introduces the word bag model which is used in the field of medical image classification in traditional statistical learning methods and the SIFT feature with strong representativeness in image field. The basic theory and application field of word bag model and the basic principle and application of SIFT are introduced in detail. Then the basic theory of deep learning and convolution neural network and its application in image classification are described. Finally, according to the characteristics of the traditional statistical learning classification method and convolution neural network method, the author explores the combination of the two methods. In the last part, we compare and analyze the experimental results of medical image classification using convolution neural network and word bag model. It shows that the convolutional neural network based on the deep learning method can not only save the work of artificial feature engineering, but also has better classification effect than the traditional statistical learning method in medical image analysis. Then, by combining the automatic feature extraction of convolution neural network and the classification ability of traditional classification methods, the medical image classification is analyzed and compared with the first two classification methods. It is verified that the classification method which combines convolution neural network based on deep learning with traditional statistical learning method has better superiority in the field of medical image classification.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP183
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