{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:16:23Z","timestamp":1766268983105,"version":"3.41.2"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We evaluated deep learning architectures, including Unetplusplus_vgg19, Unet_vgg11, and FPN_resnet34, trained on a dataset of annotated cystoscopy images of low quality.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2024.1406806","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T12:19:58Z","timestamp":1717071598000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing bladder cancer diagnosis through transitional cell carcinoma polyp detection and segmentation: an artificial intelligence powered deep learning solution"],"prefix":"10.3389","volume":"7","author":[{"given":"Mahdi-Reza","family":"Borna","sequence":"first","affiliation":[]},{"given":"Mohammad Mehdi","family":"Sepehri","sequence":"additional","affiliation":[]},{"given":"Pejman","family":"Shadpour","sequence":"additional","affiliation":[]},{"given":"Farhood","family":"Khaleghi Mehr","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"8589","DOI":"10.3390\/s23208589","article-title":"Improved UNet with attention for medical image segmentation","volume":"23","author":"Al Qurri","year":"2023","journal-title":"Sensors"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"11629","DOI":"10.1038\/s41598-021-91081-x","article-title":"Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors","volume":"11","author":"Ali","year":"2021","journal-title":"Sci. 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