Machine Learning Model of ResNet50-Ensemble Voting for Malignant-Benign Small Pulmonary Nodule Classification on Computed Tomography Images.
Weiming LiSiqi YuRunhuang YangYixing TianTianyu ZhuHaotian LiuDanyang JiaoFeng ZhangXiangtong LiuLixin TaoYan GaoQiang LiJingbo ZhangXiu-Hua GuoPublished in: Cancers (2023)
Machine learning models that were implemented and integrated ResNet50-Ensemble Voting performed exceptionally well in identifying benign and malignant small pulmonary nodules (<20 mm) from various sites, which might help doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice.
Keyphrases
- machine learning
- deep learning
- convolutional neural network
- early stage
- computed tomography
- pulmonary hypertension
- clinical practice
- artificial intelligence
- big data
- positron emission tomography
- magnetic resonance imaging
- neural network
- optical coherence tomography
- squamous cell carcinoma
- contrast enhanced
- magnetic resonance
- image quality