Lung nodule classification using radiomics model trained on degraded SDCT images
Our study demonstrates the feasibility and effectiveness of using synthetic LDCT images for developing a relatively accurate radiomics-based model in lung nodule classification. This approach addresses challenges associated with LDCT screening, offering potential implications for improving lung cancer detection and reducing false positives.
Authors
- Anna Corti (ORCID: https://orcid.org/0000-0001-9603-7825)
- Luca Mainardi (ORCID: https://orcid.org/0000-0002-6276-6314)
- Valentina Corino (ORCID: https://orcid.org/0000-0003-1825-0422)
- Jiaying Liu (ORCID: https://orcid.org/0009-0006-5451-4324)
Institutions
- Politecnico di Milano (IT)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2024-10-23
- DOI
- https://doi.org/10.1016/j.cmpb.2024.108474
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21