Open Access3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT
Author(s) -
Shengping Wang,
Rui Wang,
Shengjian Zhang,
Ruimin Li,
Yi Fu,
Xin Sun,
Yuan Li,
Xing Sun,
Xinyang Jiang,
Xiaowei Guo,
Xuan Zhou,
Chang Jia,
Weijun Peng
Publication year - 2018
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 21
eISSN - 2223-4292
pISSN - 2223-4306
DOI - 10.21037/qims.2018.06.03
Subject(s) - convolutional neural network , receiver operating characteristic , lung cancer , radiology , medicine , computed tomography , artificial intelligence , nodule (geology) , pattern recognition (psychology) , computer science , pathology , biology , paleontology
Identification of pre-invasive lesions (PILs) and invasive adenocarcinomas (IACs) can facilitate treatment selection. This study aimed to develop an automatic classification framework based on a 3D convolutional neural network (CNN) to distinguish different types of lung cancer using computed tomography (CT) data.