Background: Bladder cancer, specifically transitional cell carcinoma (TCC) polyps, presents a significant healthcare challenge worldwide. Accurate segmentation of TCC polyps in cystoscopy images is crucial for early diagnosis and urgent treatment. Deep learning models have shown promise in addressing this challenge.
Methods: We evaluated deep learning architectures, including Unetplusplus_vgg19, Unet_vgg11, and FPN_resnet34, trained on a dataset of annotated cystoscopy images of low quality.
Results: The models showed promise, with Unetplusplus_vgg19 and FPN_resnet34 exhibiting precision of 55.40 and 57.41%, respectively, suitable for clinical application without modifying existing treatment workflows.
Conclusion: Deep learning models demonstrate potential in TCC polyp segmentation, even when trained on lower-quality images, suggesting their viability in improving timely bladder cancer diagnosis without impacting the current clinical processes.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11169928 | PMC |
| http://dx.doi.org/10.3389/frai.2024.1406806 | DOI Listing |
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