Detection of Serrated Adenoma in NBI Based on Multi-Scale Sub-Pixel Convolution

Abstract

Colorectal cancer ranks third in global malignancy incidence, and serrated adenoma is a precursor to colon cancer. However, current studies primarily focus on polyp detection, neglecting the crucial discrimination of polyp nature, hindering effective cancer prevention. This study established a static image dataset for serrated adenoma (SA) and developed a deep learning SA detection model. The proposed MSSDet (Multi-Scale Sub-pixel Detection) innovatively modifies each layer of the original feature pyramid’s structure to retain high-resolution polyp features. Additionally, feature fusion and optimization modules were incorporated to enhance multi-scale information utilization, leveraging the narrow-band imaging endoscope’s ability to provide clearer colonoscopy capillary and texture images. This paper utilized 639 cases of colonic NBI endoscopic images to construct the model, achieving a mean average precision (mAP) of 86.3% for SA in the test set. The SA detection rate via this approach has significantly surpassed conventional object detection methods.