Journal
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Volume 2016, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2016/6215085
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Funding
- National Key Technology Research and Development Program of the Ministry of Science and Technology of China [2014BAI17B01]
- National Natural Science Foundation of China [61502091]
- Fundamental Research Funds for the Central Universities [N140403004, N150408001, N140407001]
- Postdoctoral Science Foundation of China [2015M570254]
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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.
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