基于混合受限玻尔兹曼机的肺结节良恶性诊断

针对传统计算机辅助诊断中肺结节的特征提取方法依靠人工设计、操作复杂、识别率低等问题，提出了一种基于混合受限玻尔兹曼机的肺结节良恶性诊断方法。首先采用多层无监督卷积受限玻尔兹曼机自动对肺结节图像进行特征学习，然后利用分类受限玻尔兹曼机对获得的特征进行良恶性分类。为避免分类受限玻尔兹曼机在训练中出现的特征同质化问题，引入了交叉熵稀疏惩罚对其进行优化。实验结果表明，该方法有效避免了手动特征提取的复杂性，在肺结节良恶性分类的准确率、敏感性、特异性、ROC曲线下面积值上均优于传统诊断方法。

Diagnosis of benign and malignant lung nodules based on hybrid restricted Boltzmann machine

For a series of problems in traditional computer-aided diagnosis methods, such as features extraction of lung nodules relying on the manual design, complex operation, low recognition rate, and so on, a diagnosis method of benign and malignant lung nodules based on hybrid restricted Boltzmann machine is proposed. Firstly, multilayer unsupervised convolutional restricted Boltzmann machine is applied in features learning from lung nodules images. Then, these features are used as the input of classification restricted Boltzmann machine to classify benign and malignant lung nodules. In order to avoid the problem of features homogenization during the classification restricted Boltzmann machine training, cross entropy sparse penalty is added to optimize it. Experimental results show that this method can effectively avoid the complexity of manual feature extraction. And it is superior to the traditional diagnostic methods in the accuracy, sensitivity, specificity and area under ROC curve values of classification of benign and malignant lung nodules.