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

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.