Classification of Lung Nodules Based on Convolutional Deep Belief Network

被引：8

作者：

Jin, Xinyu ^{[1
]}

Ma, Chunhui ^{[1
]}

Zhang, Yuchen ^{[1
]}

Li, Lanjuan ^{[1
]}

机构：

[1] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China

来源：

2017 10TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL. 1 | 2017年

关键词：

CDBN; lung nodule CT; feature extraction; classification;

D O I：

10.1109/ISCID.2017.57

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A Convolutional Deep Belief Network model is proposed. Three CRBM layers were stacked to build a CDBN. We trained the three CRBMs layer by layer using unsupervised methods. And the feature vectors generated by the CDBN were used to train a softmax classifier. It is used to do the classification of lung nodule ROIs. The accuracy, precision, recall and F1 value of the best classification results reached 92.83%, 90%, 97.30% and 93.51% respectively. The features extracted from lung nodule ROIs by the CDBN deep network model proposed in this paper have been as good as or even better than ones extracted by experienced researchers, which shows the potential of deep learning in this field.

引用

页码：139 / 142

页数：4

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