PortNet: Achieving lightweight architecture and high accuracy in lung cancer cell classification
This study significantly reduces the parameter count through an innovative model structure while maintaining high accuracy and stability, demonstrating outstanding performance in lung cancer cell classification tasks. The model holds the potential to become an efficient, accurate, and cost-effective auxiliary diagnostic tool for pathological classification of lung cancer in the future.
Authors
- Kaikai Zhao (ORCID: https://orcid.org/0000-0001-5556-2877)
- Youjiao Si (ORCID: https://orcid.org/0000-0001-8425-4216)
- Xiangjiao Meng (ORCID: https://orcid.org/0000-0003-2737-5905)
- Liangchao Sun (ORCID: https://orcid.org/0000-0003-2344-6059)
Institutions
- Shandong Tumor Hospital (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2025-01-09
- DOI
- https://doi.org/10.1016/j.heliyon.2025.e41850
- Citations
- 3
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 7.44
Funders
- National Natural Science Foundation of China