Integrating SEResNet101 and SE-VGG19 for advanced cervical lesion detection: a step forward in precision oncology
Our results indicate that SEResNet101 offers a promising enhancement over existing screening methods, integrating advanced deep learning algorithms to significantly improve the precision of cervical lesion classification. This study advocates for the inclusion of SEResNet101 in clinical workflows to enhance cervical cancer screening protocols, thereby improving patient outcomes. Future work should focus on multicentric trials to validate these findings and facilitate widespread clinical adoption.
Authors
- Feifei Ni (ORCID: https://orcid.org/0000-0002-0369-5595)
- Haizhen He (ORCID: https://orcid.org/0009-0009-0923-6267)
- Peipei Li (ORCID: https://orcid.org/0000-0003-0873-8475)
- Yuanyuan Chen (ORCID: https://orcid.org/0000-0003-2893-6313)
- Ye Yan (ORCID: https://orcid.org/0000-0003-0847-561X)
- Jiajia Pan (ORCID: https://orcid.org/0000-0003-3829-6671)
Institutions
- Wenzhou City People's Hospital (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1186/s12885-025-14353-z
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00