PD-YOLO:Colon Polyp Detection Model Based on Enhanced Small-Target Feature Extraction  

作　　者：Yicong Yu Kaixin Lin Jiajun Hong Rong-Guei Tsai Yuanzhi Huang 

机构地区：[1]New Engineering Industry College,Putian University,Putian,351100,China [2]Engineering Research Center of Big Data Application in Private Health Medicine(Putian University),Fujian Province University,Putian,351100,China [3]Computer College,Guangdong University of Science&Technology,Dongguan,523668,China

出　　处：《Computers, Materials & Continua》2025年第1期913-928,共16页计算机、材料和连续体（英文）

基　　金：funded by the Undergraduate Higher Education Teaching and Research Project(No.FBJY20230216);Research Projects of Putian University(No.2023043);the Education Department of the Fujian Province Project(No.JAT220300).

摘　　要：In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon polyps.However,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis process.In order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant parts.To help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of upsampling.Theexperimental results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp detection.Compared to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.

关 键 词：Polyp detection YOLOv7 SPD-Conv CBAM DECONVOLUTION 

分 类 号：R574[医药卫生—消化系统]