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

Authors: Qing-Shan Zhao; Guo-Hua Ji; Yu-Lan Hu; Guo-Yan Meng

Addresses: Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou, Shanxi Province, 034000, China ' Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou, Shanxi Province, 034000, China ' Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou, Shanxi Province, 034000, China ' Department of Mathematics, Xinzhou Teachers University, Xinzhou, Shanxi Province, 034000, China

Abstract: 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.

Keywords: pulmonary nodules; computer aided diagnosis; learning vector quantisation network; LVQ; artificial bee colony algorithm; ABC; feature integration.

DOI: 10.1504/IJCSM.2018.10017537

International Journal of Computing Science and Mathematics, 2018 Vol.9 No.6, pp.577 - 589

Received: 12 Dec 2017
Accepted: 21 Mar 2018

Published online: 16 Nov 2018 *

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