计算机工程与科学2017，Vol.39Issue(2)：323-329,7.DOI:10.3969/j.issn.1007-130X.2017.02.016
基于卷积受限玻尔兹曼机的医学图像分类新方法
A new medical image classification method based on convolution restricted Boltzmann machine
摘要
Abstract
Data mining methods are widely used to analyze medical images in current research.Commonly 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.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Key words
medical image classification/convolution restricted Boltzmann machine/fast continuous contrastive divergence/accuracy of classification分类
信息技术与安全科学引用本文复制引用
张娟,蒋芸,胡学伟,肖吉泽..基于卷积受限玻尔兹曼机的医学图像分类新方法[J].计算机工程与科学,2017,39(2):323-329,7.基金项目
国家自然科学基金(61163036,61163039) （61163036,61163039）
2012年度甘肃省高校基本科研业务费专项资金(1201-16) （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) （nwnu-kjcxgc-03-67）
