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. 2015 Aug 4:8:2015-22.
doi: 10.2147/OTT.S80733. eCollection 2015.

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

Kai-Lung Hua  1 , Che-Hao Hsu  1 , Shintami Chusnul Hidayati  1 , Wen-Huang Cheng  2 , Yu-Jen Chen  3
Affiliations

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

Kai-Lung Hua et al. Onco Targets Ther. .

Abstract

Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.

Keywords: convolutional neural network; deep belief network; deep learning; nodule classification.

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Figures

Figure 1
Figure 1
Various types of pulmonary nodules seen on computed tomography scan images. Lung nodules were highlighted by yellow color.
Figure 2
Figure 2
Scheme for restricted Boltzmann machine. The restricted Boltzmann machine is a fully connected bipartite graph.
Figure 3
Figure 3
Architecture of a convolutional neural network. This model was constructed from training data using the gradient back-propagation method. Abbreviation: MLP, multiple layer perceptron.
Figure 4
Figure 4
Deep belief network learning framework to circumvent elaboration of semantic features. The concept of a training nodule classifier is illustrated. Abbreviation: RBM, restricted Boltzmann machine.
Figure 5
Figure 5
Convolutional neural network structure: connectivity pattern between layers.
Figure 6
Figure 6
Deep belief network structure. Abbreviation: RBM, restricted Boltzmann machine.
See this image and copyright information in PMC

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