Classification of pulmonary nodules using neural network ensemble

Authors:
Hui Chen;Wenfang Wu;Hong Xia;Jing Du;Miao Yang;Binrong Ma
Affiliations:
School of Biomedical Engineering, Capital Medical University, Beijing, China;School of Biomedical Engineering, Capital Medical University, Beijing, China;School of Biomedical Engineering, Capital Medical University, Beijing, China;School of Biomedical Engineering, Capital Medical University, Beijing, China;School of Biomedical Engineering, Capital Medical University, Beijing, China;School of Biomedical Engineering, Capital Medical University, Beijing, China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Year:
2011

Citing 5
Cited 0

A knowledge-based artificial neural network classifier for pulmonary embolism diagnosis

Computers in Biology and Medicine
A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks

Journal of Medical Systems
Diagnosis of valvular heart disease through neural networks ensembles

Computer Methods and Programs in Biomedicine
Predicting breast cancer survivability: a comparison of three data mining methods

Artificial Intelligence in Medicine
Lung cancer cell identification based on artificial neural network ensembles

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

A neural network ensemble (NNE) scheme was designed for distinguishing probably benign, uncertain and probably malignant lung nodules on thin-section CT images. To construct the NNE scheme, a multilayer neural network with the back-propagation algorithm (BPNN), a radial basis probabilistic neural network (RBPNN) and a learning vector quantization neural network (LVQNN) were employed, and the Bayesian criterion was used as combination rule to integrate the outputs of individual neural networks. Experimental results illustrated that the proposed classification scheme had higher classification accuracy (78.7%) as compared to the best individual classifier (LVQNN: 68.1%), as well as to another NNE scheme with the same individual neural networks but with majority voting combination rule.