LUNG NODULE DETECTION ALGORITHM INTEGRATING ATTENTION AND RESIDUAL FEEDBACK
As the early manifestation of lung cancer,lung nodules are of different sizes.The rapid and accurate detection of lung nodules is not only of great significance for the prevention and treatment of lung cancer,but also an arduous task.To this end,a method to improve YOLOv3 is proposed.In view of the problem of different sizes of lung nodules,the multi-scale feature fusion structure was improved,which increased from 3-scale prediction to 4-scale to obtain more small-scale lung nodule feature information.The attention mechanism was added to improve the ability to extract effective information,and the loop-back residual structure was introduced to enhance the effective information and accelerate the convergence speed of the network.CIoU was selected as the bounding box loss function.Experimental analysis shows that compared with the original algorithm,the improved algorithm's mAP has increased by 0.063,and the FPs has increased by 4.8 frames/s,and the detection effect of lung nodules is improved.Compared with other algorithms,it has better accuracy and real-time performance.
万方数据
维普
