Machine Learning Model of ResNet50-Ensemble Voting for Malignant–Benign Small Pulmonary Nodule Classification on Computed Tomography Images

Background: The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients’ quality of living. Machine learning methods performed admirably when recognizing small benign and m...

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Bibliographic Details
Published in:Cancers 2023-11, Vol.15 (22), p.5417
Main Authors: Li, Weiming, Yu, Siqi, Yang, Runhuang, Tian, Yixing, Zhu, Tianyu, Liu, Haotian, Jiao, Danyang, Zhang, Feng, Liu, Xiangtong, Tao, Lixin, Gao, Yan, Li, Qiang, Zhang, Jingbo, Guo, Xiuhua
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Benign
Classification
Computed tomography
CT imaging
Deep learning
Learning algorithms
Lung cancer
Lung diseases
Lung nodules
Machine learning
Medical prognosis
Medical screening
Mortality
Patients
Radiomics
Tomography
Voting
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Summary:Background: The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients’ quality of living. Machine learning methods performed admirably when recognizing small benign and malignant lung nodules. However, exploration and investigation are required to fully leverage the potential of machine learning in distinguishing between benign and malignant small lung nodules. Objective: The aim of this study was to develop and evaluate the ResNet50-Ensemble Voting model for detecting the benign and malignant nature of small pulmonary nodules (
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers15225417