Pulmonary nodules computer-aided diagnosis based on feature integration and ABC-LVQ network

作     者：Zhao, Qing-Shan Ji, Guo-Hua Hu, Yu-Lan Meng, Guo-Yan 

作者机构：Xinzhou Teachers Univ Dept Comp Sci & Technol Xinzhou 034000 Shanxi Peoples R China Xinzhou Teachers Univ Dept Math Xinzhou 034000 Shanxi Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS》 (国际计算科学与数学杂志)

年 卷 期：2018年第9卷第6期

页      面：577-589页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of Shanxi Province [2014011019-3] Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province 

主　　题：pulmonary nodules computer aided diagnosis learning vector quantisation network LVQ artificial bee colony algorithm ABC feature integration 

摘      要：For the computer aided diagnosis of lung cancer, a malignancy identification method based on multi-feature integration and learning vector quantisation (LVQ) network optimised by artificial bee colony (ABC) is proposed in this work. Firstly, the traditional features and the hidden features learned by Sparse Autoencoder of nodules are respectively extracted, and then the canonical correlation analysis (CCA) is used for feature integration. For classification, the ABC algorithm is used to optimise the LVQ network to overcome its sensitivity to initial value. Finally, the integrated features of nodules are input into the optimised classifier and the diagnosis results are obtained. Experimental results on LIDC pulmonary nodule image datasets show that this method can effectively identify the malignancy of nodules, with the area under the receiver operating characteristic (ROC) curve (AUC) of 0.90, 0.83, 0.80, 0.80, 0.85 for nodules of malignancy 1-5 classification, respectively.