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Open Access

Peer-reviewed

Research Article

Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm

Ming Ding ,

Contributed equally to this work with: Ming Ding, Shi-yu Pan

Roles Conceptualization, Resources, Writing – original draft

Affiliation Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
⨯
Shi-yu Pan ,

Contributed equally to this work with: Ming Ding, Shi-yu Pan

Roles Formal analysis, Methodology, Writing – original draft

Affiliation School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China
⨯
Jing Huang,

Roles Data curation

Affiliation Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
⨯
Cheng Yuan,

Roles Data curation

Affiliation Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
⨯
Qiang Zhang,

Roles Data curation

Affiliation Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
⨯
Xiao-li Zhu,

Roles Data curation

Affiliation Department of Respiratory Medicine, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China
⨯
Yan Cai

Roles Conceptualization, Writing – review & editing

* E-mail: yancai@seu.edu.cn

Affiliation School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China

https://orcid.org/0000-0002-7269-7375

⨯

Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm

Ming Ding,
Shi-yu Pan,
Jing Huang,
Cheng Yuan,
Qiang Zhang,
Xiao-li Zhu,
Yan Cai

x

Published: December 31, 2021
https://doi.org/10.1371/journal.pone.0260600

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Lung and intrathoracic tumors
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Pulmonary imaging
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Inflammation
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Adenocarcinoma
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Cancer detection and diagnosis
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Lesions
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Machine learning algorithms
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Pneumonia
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