Improved YOLOv7 Algorithm for Colorectal Polyp Detection
Computer-aided diagnosis is essential to improve polyp diagnostic accuracy and reduce colorectal cancer mortality,but the variety of polyp morphologies,polyp analogs and the complex environment in the bowel lead to more misdiagnosis and underdiagnosis with current methods.Therefore,an improved YOLOv7 colorectal polyp detection algo-rithm(YOLOv7-IDH)is proposed,which firstly,efficient decoupled heads with implicit knowledge are used to make full use of the implicit information and to prevent mutual interference between classification and regression tasks;then,global attention mechanism is introduced to enhance the model's capability of extracting shallow features;finally,the SPPCSPC module is optimized to reduce the model parameters and to improve the convergence speed.The experimental results show that the Fl score and mAP@0.5 of the improved model on the combined dataset reach 94.8％and 97.1％,respectively,which can meet the requirements for automatic polyp detection.
万方数据
维普
