Pulmonary Nodules Detection Based on Deformable Convolution

Early detection of malignant pulmonary nodules is of great help to the treatment of lung cancer. Yet it is difficult to establish a general diagnostic standard because of the two main characteristics of pulmonary nodules: different sizes and irregular shapes. To address this problem effectively, an improved pulmonary nodule detection model based on deformable convolution is proposed. Specifically, by adding a branch network to obtain the offsets, the process of feature extraction is more suitable with the shape of nodule itself. Besides, a simple but effective strategy is proposed for the size variability of pulmonary nodules, which is combined with the multilevel information as well as the fusion of different sizes feature maps. Compared with the two-dimensional convolution neural network and other advanced technologies, our method has a significant improvement, and its mean average precision can achieve 82.7%.