Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images
Joint Authors
Zhao, Dazhe
Li, Wei
Cao, Peng
Wang, Junbo
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-14
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Wei& Cao, Peng& Zhao, Dazhe& Wang, Junbo. 2016. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1100161
Modern Language Association (MLA)
Li, Wei…[et al.]. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1100161
American Medical Association (AMA)
Li, Wei& Cao, Peng& Zhao, Dazhe& Wang, Junbo. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1100161
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100161