3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT | Zendy

Shengping Wang | Zendy; Rui Wang | Zendy; Shengjian Zhang | Zendy; Ruimin Li | Zendy; Yi Fu | Zendy; Xiangjie Sun | Zendy; Li Yuan | Zendy; Xing Sun | Zendy; Xinyang Jiang | Zendy; Xiaowei Guo | Zendy; Xuan Zhou | Zendy; Chang Jia | Zendy; Weijun Peng | Zendy

Open Access

3D 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,

Xiangjie Sun,

Li Yuan,

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.

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