CT影像下的肺结节分类方法研究综述
Review of Classification Methods for Lung Nodules in CT Images
摘要
Abstract
In recent years,deep learning has been widely applied to various classification tasks due to its capability in automatically extracting features and superior classification performance.Research on the classification of lung nodules has gradually shifted from traditional methods that involve manual feature extraction to deep learning-based classification approaches.To better investigate the benign-malignant classification of lung nodules in CT images,the current status of deep learning methods based on convolutional neural network(CNN)in the research of benign and malignant classification of lung nodules is summarized.Firstly,this paper introduces commonly used publicly available datasets for lung nodule classification,including their contents,limitations,and download sources.Secondly,it outlines commonly used performance evaluation metrics.It then highlights the recent research work on deep learning methods for lung nodule classification:current methods for lung nodule classification are categorized as using only CNN,introducing an attention mechanism in CNN,multi-view learning,multi-modal learning,and using migration learning,adversarial neural networks,and other methods,repectively,from the level of network structure and data.Meanwhile,this paper further summarizes the network structures,advantages and disadvantages of these classifica-tion methods.A comparative analysis is conducted on the benign-malignant classification performance of lung nod-ule classification methods based on these aspects over the past three years using publicly available nodule datasets.Finally,this paper discusses current challenges and explores further research directions in the field of lung nodule classification.关键词
深度学习/肺结节分类/卷积神经网络(CNN)/特征提取Key words
deep learning/lung nodule classification/convolutional neural networks(CNN)/feature extraction分类
信息技术与安全科学引用本文复制引用
利建铖,曹路,何锡权,廖军红..CT影像下的肺结节分类方法研究综述[J].计算机科学与探索,2024,18(7):1705-1724,20.基金项目
国家自然科学基金(61771347) （61771347）
广东普通高校重点领域专项(2022DZX1033) （2022DZX1033）
广东省联合培养研究生示范基地项目(YJS-SFJD-22-01) （YJS-SFJD-22-01）
广东省教育科学规划课题(高等教育专项)(2022GXJK350) （高等教育专项）
江门市基础与理论科学研究类科技计划项目(2022JC01022,江科[2023]111号) （2022JC01022,江科[2023]111号）
2022年度江门市医疗卫生领域科技计划项目(2022YL01029) （2022YL01029）
五邑大学大学生创新创业训练计划项目(202311349135,202311349153).This work was supported by the National Natural Science Foundation of China(61771347),the Basic Research Key Project in General Colleges and Universities of Guangdong Province(2022DZX1033),the Demonstration Base Project of Guangdong Province Joint Training Graduate(YJS-SFJD-22-01),the Education Science Planning Topic of Guangdong Province(Higher Education Special Project)(2022GXJK350),the Basic and Theoretical Scientific Research Science and Technology Plan Project of Jiangmen(2022JC01022,[2023]111),the Medical and Health Sector Science and Technology Plan Project of Jiangmen in 2022(2022YL01029),and the College Student Innovation and Entrepreneurship Training Program of Wuyi University(202311349135,202311349153). （202311349135,202311349153）
