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Comput Math Methods Med. 2016;2016:6215085. doi: 10.1155/2016/6215085. Epub 2016 Dec 14.

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

Author information

1
Medical Image Computing Laboratory of Ministry of Education, Northeastern University, Shenyang 110819, China; College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
2
Neusoft Research Institute, Neusoft Corporation, Shenyang 110179, China.

Abstract

Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

PMID:
28070212
PMCID:
PMC5192289
DOI:
10.1155/2016/6215085
[Indexed for MEDLINE]
Free PMC Article

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