PVT-v2-B2-FPN: A Multi-Resolution Transformer-Based Framework for Accurate Colorectal Polyp Segmentation

Colorectal cancer remains one of the leading causes
of cancer-related mortality, making accurate polyp segmentation
important for early diagnosis and treatment. This study proposes
a PVT-v2-B2 + FPN framework for automatic colorectal polyp
segmentation. The model combines a hierarchical transformer
encoder with feature pyramid decoding to integrate global
context and multi-scale spatial features for accurate boundary
localization. Geometric and photometric augmentations were ap-
plied to improve robustness under diverse colonoscopy conditions.
Experiments were conducted on Kvasir-SEG using a 700/150/150
train-validation-test split with multi-seed and multi-resolution
evaluation. The selected proposed configuration achieved a test
Dice score of 91.05% and IoU of 83.71%, while the best
individual run reached 91.08% Dice. Compared with CNN-,
transformer-, and hybrid-based baselines, the proposed model
showed competitive segmentation performance with 29.8M pa-
rameters. These results indicate that hierarchical transformer
features with FPN-based fusion can provide an effective balance
between segmentation accuracy and computational practicality
for colonoscopy-assisted polyp analysis