  Polyp segmentation within colonoscopy video frames using deep learning models
has the potential to automate the workflow of clinicians. This could help
improve the early detection rate and characterization of polyps which could
progress to colorectal cancer. Recent state-of-the-art deep learning polyp
segmentation models have combined the outputs of Fully Convolutional Network
architectures and Transformer Network architectures which work in parallel. In
this paper we propose modifications to the current state-of-the-art polyp
segmentation model FCBFormer. The transformer architecture of the FCBFormer is
replaced with a SwinV2 Transformer-UNET and minor changes to the Fully
Convolutional Network architecture are made to create the FCB-SwinV2
Transformer. The performance of the FCB-SwinV2 Transformer is evaluated on the
popular colonoscopy segmentation bench-marking datasets Kvasir-SEG and
CVC-ClinicDB. Generalizability tests are also conducted. The FCB-SwinV2
Transformer is able to consistently achieve higher mDice scores across all
tests conducted and therefore represents new state-of-the-art performance.
Issues found with how colonoscopy segmentation model performance is evaluated
within literature are also re-ported and discussed. One of the most important
issues identified is that when evaluating performance on the CVC-ClinicDB
dataset it would be preferable to ensure no data leakage from video sequences
occurs during the training/validation/test data partition.
