Abstract:

Lung nodules occupy fewer pixels in CT(Computed Tomography), which brings great difficulty to detection. In view of small target detection of lung nodule, this study proposes YOLOv3(You Only Look Once version 3) lung nodule detection algorithm based on coordinate attention. The backbone network adopts the improved YOLOv3, reduces the number of residual blocks and introduces the dilated convolution module to sense context information around the target. In the feature utilization, coordinate attention is introduced to capture the position, direction and cross-channel information, so as to locate lung nodules accurately. The loss function of YOLOv3 is improved, the boundary box is modeled as Gaussian distribution. Wasserstein distance is used to calculate the similarity between the two distributions instead of IoU(Intersection over Union), so as to improve the sensitivity of the target scale. The results on LUNA16 show that the average precision is 89.96% and the sensitivity is 95.37%. Compared with mainstream target detection algorithms, the precision and sensitivity of the proposed method are improved by 11.33% and 9.03%, respectively.

Key words: lung nodules, YOLOv3, dilated convolution, coordinate attention, small target detection, squeeze-and-excitation networks, CBAM, NWD