  Identifying peri-pancreatic edema is a pivotal indicator for identifying
disease progression and prognosis, emphasizing the critical need for accurate
detection and assessment in pancreatitis diagnosis and management. This study
\textit{introduces a novel CT dataset sourced from 255 patients with pancreatic
diseases, featuring annotated pancreas segmentation masks and corresponding
diagnostic labels for peri-pancreatic edema condition}. With the novel dataset,
we first evaluate the efficacy of the \textit{LinTransUNet} model, a linear
Transformer based segmentation algorithm, to segment the pancreas accurately
from CT imaging data. Then, we use segmented pancreas regions with two
distinctive machine learning classifiers to identify existence of
peri-pancreatic edema: deep learning-based models and a radiomics-based eXtreme
Gradient Boosting (XGBoost). The LinTransUNet achieved promising results, with
a dice coefficient of 80.85\%, and mIoU of 68.73\%. Among the nine benchmarked
classification models for peri-pancreatic edema detection, \textit{Swin-Tiny}
transformer model demonstrated the highest recall of $98.85 \pm 0.42$ and
precision of $98.38\pm 0.17$. Comparatively, the radiomics-based XGBoost model
achieved an accuracy of $79.61\pm4.04$ and recall of $91.05\pm3.28$, showcasing
its potential as a supplementary diagnostic tool given its rapid processing
speed and reduced training time. Our code is available
\url{https://github.com/NUBagciLab/Peri-Pancreatic-Edema-Detection}.
