A survey on lung CT datasets and research trends
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42600-021-00138-3.pdf
Reference100 articles.
1. Abbas Q. Nodular-deep: classification of pulmonary nodules using deep neural network. Int. J.Med. Res. Heal. Sci. 2017.
2. Aerts HJWL, et al. Decoding tumor phenotype by non-invasive imaging using a quantitative radiomics approach. Nat. Commun. 2014;5.
3. Akram S, Javed MY, Akram MU, Qamar U, Hassan A. Pulmonary nodules detection and classification using hybrid features from computerized tomographic images. J Med Imaging Heal Informatics. 2016.
4. Akram S, Javed MY, Qamar U, Khanum A, Hassan A Artificial neural network-based classification of lungs nodule using hybrid features from computerized tomographic images. Appl Math Inf Sci , 2015.
5. Anitha SAAJ, Peter JD. Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Comput & Applic. 2018;32:15845–55. https://doi.org/10.1007/s00521-018-3877-3.
Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images;International Journal of Computer Assisted Radiology and Surgery;2024-07-14
2. Temporal Stability and Prognostic Power of Radiomic Features for Survival Analysis in Lung Adenocarcinoma;Traitement du Signal;2023-12-30
3. A quantitative analysis of imaging features in lung CT images using the RW-T hybrid segmentation model;Multimedia Tools and Applications;2023-10-02
4. A survey and taxonomy of 2.5D approaches for lung segmentation and nodule detection in CT images;Computers in Biology and Medicine;2023-10
5. Annotated Lung CT Image Database;2022 International Symposium ELMAR;2022-09-12
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3