DCA-YOLO: Detection Algorithm for YOLOv8 Pulmonary Nodules Based on Attention Mechanism Optimization

Abstract

Pulmonary nodules represent an early manifestation of lung cancer. However, pulmonary nodules only constitute a small portion of the overall image, posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections. To solve these problems, the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP), along with the integration of a coordinate attention(CA) mechanism. This allows the network to focus on small targets while expanding the receptive field without losing resolution. At the same time, context information on the target is gathered and feature expression is enhanced by attention modules in different directions. It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset. Compared with other detection algorithms, it improves the accuracy of pulmonary nodule detection to a certain extent.

Keywords

pulmonary nodule / YOLOv8 network / object detection / deformable convolution / atrous spatial pyramid pooling (ASPP) / coordinate attention (CA) mechanism

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