A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning.

Journal: PloS one

Published Date: Jul 12, 2019

Abstract

BACKGROUND: Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer. Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively.

Authors

Wenkai Huang

Center for Research on Leading Technology of Special Equipment, School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou, P.R. China.
Yihao Xue

School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou, P.R. China.
Yu Wu

Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan, 430079, People's Republic of China.

Keywords

Diagnosis, Computer-Assisted Early Detection of Cancer False Positive Reactions Humans Imaging, Three-Dimensional Lung Neoplasms Multiple Pulmonary Nodules Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted Reference Standards Solitary Pulmonary Nodule Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (31299053)

A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning.

Abstract

Authors

Keywords

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