Evaluation of recent lightweight deep learning architectures for lung cancer CT classification
The lightweight models demonstrated superior efficiency and comparable performance to traditional models, making them ideal for deployment in low-resource settings where computational resources are limited. These findings highlight the potential for practical use in clinical workflows, overcoming barriers associated with traditional models.
Authors
- Yuling Liufu
- Yanhua Wen
- Yubao Guan
- Xiaohuan Pan
- Mostafa Mahmoud (ORCID: https://orcid.org/0000-0002-9100-7966)
Institutions
- Second Affiliated Hospital of Guangzhou Medical University (CN)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-09-19
- DOI
- https://doi.org/10.3389/fonc.2025.1647701
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00