title: Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.
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.
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, Shenyang 110819, China; College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, Shenyang 110819, China; College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
authors.forename: Peng
authors.identifier: 0000-0002-7859-2769
authors.initials: P
authors.lastname: Cao
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, Shenyang 110819, China; College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
authors.forename: Dazhe
authors.initials: D
authors.lastname: Zhao
authors.affiliation: Neusoft Research Institute, Neusoft Corporation, Shenyang 110179, China.
authors.forename: Junbo
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D016208:Databases, Factual; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed