Pancreas Region Object Detection Based on Faster R-CNN

Lang He; Lijia Wang

doi:10.47297/taposatWSP2633-456914.20260703

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Pancreas Region Object Detection Based on Faster R-CNN

Volume 7, Issue 3

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Pancreas Region Object Detection Based on Faster R-CNN
Pancreas Region Object Detection Based on Faster R-CNN
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
Vol. 7, Issue 3, Pages: 67-72(2026)
Published：30 March 2026
DOI：10.47297/taposatWSP2633-456914.20260703
Accepted：

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He Lang,Wang Lijia.Pancreas Region Object Detection Based on Faster R-CNN[J].Theory and Practice of Science and Technology,2026,07(03):67-72.
DOI：

He Lang,Wang Lijia.Pancreas Region Object Detection Based on Faster R-CNN[J].Theory and Practice of Science and Technology,2026,07(03):67-72. DOI： 10.47297/taposatWSP2633-456914.20260703.

摘要

Abstract

To address the challenges of low pancreas voxel ratio

complex background interference

and low contrast with surrounding organs in abdominal CT images

this study proposes an improved Faster R-CNN-based region of interest (ROI) detection algorithm

PPA-FRCNN

to provide high-quality regional input for fine pancreas segmentation. The algorithm replaces the traditional VGG-16 with ResNet50 as the backbone network

enhancing deep feature representation through residual connections. A dual-pyramid feature network structure is introduced to construct bidirectional feature aggregation paths from bottom-up and top-down directions

achieving cross-level multi-scale fusion of shallow edge textures and deep semantic information. A multi-branch multi-attention joint enhancement mechanism is designed to collaboratively strengthen pancreas feature representation from channel

context

and spatial dimensions. RoI Pooling is replaced with RoI Align to eliminate pixel quantization errors and optimize pancreas bounding box regression accuracy. Experimental results on the public NIH-TCIA dataset and clinical private dataset demonstrate that PPA-FRCNN achieves mAP50 of 89.7% and 90.7% respectively

with precision and recall rates superior to traditional Faster R-CNN

YOLOv7

and other comparative models

enabling high-precision localization of pancreas ROI and effectively reducing background interference and computational redundancy for subsequent segmentation tasks.

关键词

Keywords

references

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