Artificial Intelligence-Based Classification and Segmentation of Bladder Cancer in Cystoscope Images
In this study, we applied two deep learning models using well-annotated datasets of cystoscopic images. Both VGG19 and Deeplab v3+ demonstrated high performance in classification and segmentation, respectively. These models can serve as valuable tools for bladder cancer research and may aid in the diagnosis of bladder cancer.
Authors
- Seon Beom Jo (ORCID: https://orcid.org/0009-0002-7393-2936)
- Du Geon Moon (ORCID: https://orcid.org/0000-0002-9031-9845)
- Insung Choi (ORCID: https://orcid.org/0009-0000-0995-6947)
- Mi Mi Oh (ORCID: https://orcid.org/0000-0002-1232-2598)
- Wonku Hwang (ORCID: https://orcid.org/0009-0000-0415-5489)
- Da Eun Han (ORCID: https://orcid.org/0000-0002-6395-5651)
- Sun Tae Ahn (ORCID: https://orcid.org/0000-0003-1233-5951)
- Jong Wook Kim (ORCID: https://orcid.org/0000-0003-2228-0640)
- Zepa Yang (ORCID: https://orcid.org/0000-0001-8089-7396)
- Hong Seok Park (ORCID: https://orcid.org/0000-0001-7287-2682)
Institutions
- Korea University (KR)
- Korea University Medical Center (KR)
Publication Details
- Journal
- Cancers
- Published
- 2024-12-28
- DOI
- https://doi.org/10.3390/cancers17010057
- Citations
- 11
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 6.30