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Kai-Lung Hua,1 Che-Hao Hsu,1 Shintami Chusnul Hidayati,1 Wen-Huang Cheng,2 Yu-Jen Chen3 1Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 2Research Center for Information Technology Innovation, Academia Sinica, 3Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan 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: nodule classification, deep learning, deep belief network, convolutional neural network |
