Detection method of pulmonary nodules based on improved residual structure
SHI Lukui1, MA Hongqi1, ZHANG Chaozong2, FAN Shiyan1
1. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China; 2. Hebei Science and Technology Information Institute, Shijiazhuang Hebei 050000, China
Abstract：In order to solve the problems of high computing cost and over-fitting of the model caused by complicated network structure in pulmonary nodule detection method, an improved residual network structure combining deep separable convolution and pre-activation was proposed. And the proposed network structure was applied to a pulmonary nodule detection model. Based on the target detection network Faster R-CNN, with U-Net coder-decoder structure adopted, the deep separable convolution and pre-activation operations were used by the model to improve the 3D residual network structure. Firstly, with the use of deep separable convolution, the complexity and computing cost of the model were reduced. Then, the regularization of the model was improved by introducing the pre-activation operation, and the phenomenon of overfitting was alleviated. Finally, the rectangular convolution kernel was used to expand the receptive field of the convolution operation on the premise that the computing cost of the model was slightly increased, so as to effectively take into account both the global and local characteristics of the pulmonary nodules. On the LUNA16 dataset, the proposed method has the sensitivity of 96.04%, and the Free-response area under the Receiver Operating Characteristic curve (FROC) score of 83.23%. The experimental results show that the method improves the sensitivity of pulmonary nodule detection, effectively reduces the average number of false positives in the detection results, and improves the detection efficiency. This proposed method can effectively assist radiologists in detecting pulmonary nodules.
石陆魁, 马红祺, 张朝宗, 樊世燕. 基于改进残差结构的肺结节检测方法[J]. 计算机应用, 2020, 40(7): 2110-2116.
SHI Lukui, MA Hongqi, ZHANG Chaozong, FAN Shiyan. Detection method of pulmonary nodules based on improved residual structure. Journal of Computer Applications, 2020, 40(7): 2110-2116.
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2020, Vol. 40 
