PraNet-based Gastrointestinal Polyp Segmentation on Heterogeneous Datasets: Study of Augmentation Impact on Performance

Deep Learning (DL) in the healthcare is exemplified by specific applications such as colorectal polyp segmentation using the Parallel Reverse Attention Network (PraNet) model. The main objective of this research consisted on investigating how data augmentation impacts the performance of the PraNet model across heterogeneous datasets. It is proposed a data augmentation methodology with properties to conserve the original image quality. Seven public polyp image datasets with a unified normalization pipeline and specific augmentation techniques were applied to expand the training data. Four experiments were conducted: (1) baseline training on each normalized dataset; (2) training on each dataset with augmentation; (3) combined training on all normalized datasets, tested on an independent dataset to assess generalization; and (4) combined training on all normalized plus augmented data, tested on the same independent set to examine augmentation-induced bias. The results show that the specific augmentation substantially improves in-domain segmentation accuracy under five metrics Dice, IoU, MAE, S-measure and E-measure. Average Dice score gains of 0.20–0.25 on small datasets, and training on the aggregated multi-source data yields strong generalization (Dice ≈ 0.81) on the independent set. When augmentation is added to combined training, performance on the independent test slightly decreases, the Dice score decreases from 0.81 to 0.77, suggesting a slight decrease in segmentation performance under varying test conditions, indicating potential overfitting or distribution bias introduced by excessive augmentation. Combining multiple datasets resulted in a model with strong generalization to an independent dataset, where real data diversity can effectively limit the bias.