摘要
利用数据挖掘方法对医学图像做分析是目前研究的热点之一,常用的挖掘方法首先需要从医学图像中提取特征,然后进行分类分析。目前,应用最多的是提取图像的统计特征,这种方法对所提取的特征有很强的依赖性。采用一种深度学习的新方法——卷积受限玻尔兹曼机模型,并且采用改进的快速持续对比散度算法对模型进行训练。该方法直接从乳腺X光图像中自主学习特征并利用学习到的特征对图像进行分类。实验结果显示,新方法对医学图像的分类精度相对于已有方法有明显的提升。
Data mining methods are widely used to analyze medical images in current research. Com- monly used mining methods first need to extract features from medical images and then do classification analysis. At present, the statistical features extracted from images are mostly applied, however, it has a strong dependence on the extracted features. We propose a new classification method based on convolution restricted Boltzmann machine （CRBM）, which can train the CRBM model by the fast continuous contrastive divergence algorithm. The method can directly and automatically learn features from the mammography image and use these features to do classificature. Experimental results show that the proposed method can improve the classification accuracy of medical images.
出处
《计算机工程与科学》
CSCD
北大核心
2017年第2期323-329,共7页
Computer Engineering & Science
基金
国家自然科学基金(61163036
61163039)
2012年度甘肃省高校基本科研业务费专项资金(1201-16)
西北师范大学第三期知识与创新工程科研骨干项目(nwnu-kjcxgc-03-67)
关键词
医学图像分类
卷积受限玻尔兹曼机
快速持续对比散度
分类精度
medical image classification
convolution restricted Boltzmann machine
fast continuous contrastive divergence
accuracy of classification
