Utilizing Detectron2 for accurate and efficient colon cancer detection in histopathological images
These results highlight Detectron2's effectiveness as a powerful tool for computer-aided diagnostics in colon cancer detection. The approach shows strong potential for integration into clinical workflows, aiding pathologists in early diagnosis and contributing to improved patient outcomes. This study also illustrates the transformative impact of advanced machine learning techniques on medical imaging and cancer diagnostics.
Authors
- Rongzhou Li (ORCID: https://orcid.org/0009-0000-4543-2673)
- Jie Shen (ORCID: https://orcid.org/0000-0002-6021-7151)
- Xinyue Chen (ORCID: https://orcid.org/0009-0003-5015-1579)
- Xinyu Li (ORCID: https://orcid.org/0000-0003-4388-4240)
- Xiaoyun Gao
- Xiaotian Pan (ORCID: https://orcid.org/0000-0002-9473-320X)
- Xiaosheng Jin (ORCID: https://orcid.org/0000-0002-8246-2238)
- Luxi Chen (ORCID: https://orcid.org/0000-0002-8880-3347)
Institutions
- Wenzhou Medical University (CN)
- Ruian People's Hospital (CN)
- China Telecom (China) (CN)
- Hangzhou Dianzi University (CN)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2025-08-22
- DOI
- https://doi.org/10.3389/fbioe.2025.1593534
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- Wenzhou Municipal Science and Technology Bureau
- Wuyi University
- Natural Science Foundation of Zhejiang Province