摘要
肺结节在CT(Computed Tomography)图像中所占像素较少,增加了检测难度。针对肺结节小目标检测问题,文中提出了融合坐标注意力机制的YOLOv3(You Only Look Once version 3)肺结节检测算法。主干网络采用改进YOLOv3,减少残差块数量并引入扩张卷积模块,并可从目标周围感知上下文信息。在特征利用部分引入坐标注意力机制,捕捉肺结节位置、方向和跨通道信息,精确定位肺结节。改进YOLOv3的损失函数,将边界框建模成高斯分布,利用Wasserstein距离来计算两个分布之间的相似度代替IoU(Intersection over Union)度量,提升模型对目标尺度的敏感性。在LUNA16数据集上的结果显示,肺结节检测的平均精度为89.96%,敏感性为95.37%,与主流目标检测算法相比,精度平均提升了11.33%,敏感性平均提升了9.03%。
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.
作者
王新宇
赵静文
刘翔
石蕴玉
佘云浪
WANG Xinyu;ZHAO Jingwen;LIU Xiang;SHI Yunyu;SHE Yunlang(School of Electronic and Electric Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Department of Thoracic Surgery,Shanghai Pulmonary Hospital,Tongji University,Shanghai 200433,China)
出处
《电子科技》
2024年第6期1-7,共7页
Electronic Science and Technology
基金
上海市自然科学基金(19ZR1421500)。
