%0 Journal Article
%T Enhancing bladder cancer diagnosis through transitional cell carcinoma polyp detection and segmentation: an artificial intelligence powered deep learning solution.
%A Borna MR
%A Sepehri MM
%A Shadpour P
%A Khaleghi Mehr F
%J Front Artif Intell
%V 7
%N 0
%D 2024
%M 38873177
暂无%R 10.3389/frai.2024.1406806
%X <B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>We evaluated deep learning architectures, including Unetplusplus_vgg19, Unet_vgg11, and FPN_resnet34, trained on a dataset of annotated cystoscopy images of low quality.<BR><B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.