A Novel Automatic Lung Nodule Classification Scheme using Fusion Ghost Convolution and Hybrid Normalization in Chest CTs
Compared with existing methods, the proposed method can greatly improve the classification of pulmonary nodules and can effectively assist doctors in diagnosing patients with pulmonary nodules.
Authors
- Xiaoqi Lu (ORCID: https://orcid.org/0000-0001-9361-1932)
- Jianjun Li (ORCID: https://orcid.org/0000-0001-6658-9709)
- Lidong Yang (ORCID: https://orcid.org/0000-0002-5587-3744)
- Xin Liu (ORCID: https://orcid.org/0000-0002-2242-6139)
- Siyuan Tang (ORCID: https://orcid.org/0000-0002-2684-7598)
- Yu Gu (ORCID: https://orcid.org/0000-0003-4345-6932)
- Jiaqi Liu (ORCID: https://orcid.org/0000-0002-9775-2342)
- Nan Wang (ORCID: https://orcid.org/0009-0003-7036-6817)
- Baohua Zhang (ORCID: https://orcid.org/0000-0001-7169-7579)
- Qun He (ORCID: https://orcid.org/0000-0002-2110-9721)
- Jing Wang (ORCID: https://orcid.org/0000-0001-7431-8925)
Institutions
- Beijing Institute of Technology (CN)
- Inner Mongolia University of Science and Technology (CN)
- Baotou Medical College (CN)
- Inner Mongolia University of Technology (CN)
Publication Details
- Journal
- Current Medical Imaging Formerly Current Medical Imaging Reviews
- Published
- 2025-05-05
- DOI
- https://doi.org/10.2174/0115734056330120250310053454
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China