Research On Lung Nodules Detection Based On Aggregation Unet Deep Network

Lung cancer is the number one killer of cancer diseases in China.According to statistics,the 5-year survival rate is 15%.Early detection,early diagnosis and early treatment can greatly improve the survival rate of lung cancer patients.The pulmonary nodule image is an important early clinical feature of lung cancer,and it has the characteristics of small volume,uneven distribution and irregular shape.With the rapid development of CT technology and image resolution improvement,CT has also brought a lot of data,per patient per scan usually have hundreds or even thousands of sheets CT image.It’s also difficult for experimental doctors to achieve fast and accurate judgment.In this context,computer-aided detection of lung nodules based on CT images has become a research hotspot.The nodule regions contained in CT images can be automatically labeled to remind doctors to focus on viewing,thus reducing the workload of doctors and effectively improving the accuracy and efficiency of nodule detection.Based on the CT image of the lung and the idea of computer-aided detection and diagnosis,this paper proposes a lung nodule detection technique based on the aggregated Unet deep network.The technique is mainly divided into three parts,namely,the segmentation of lung parenchyma,the segmentation of pulmonary nodules,and classification of pulmonary nodules.First,we extract the lung parenchyma area based on Kmeans clustering algorithm and morphological operation,this part can reduce the nodule detection area,and improve the detection speed;On this basis,to further improve the accuracy of detection,we propose a lung nodule classification algorithm based on migration learning and support vector machine.The main research contents of this paper are as follows:First,Segmentation of lung parenchyma based on Kmeans method.When the lung parenchymal segmentation is based on the traditional threshold algorithm,it is difficult to find the optimal threshold because of the different CT acquisition devices in the dataset.Therefore,the Kmeans algorithm is used to complete the accurate segmentation of lung parenchyma,and the morphological operation algorithm is used.The lung parenchyma was repaired to make the segmentation more complete.The experimental results show that Kmeans clustering and morphological manipulation algorithms can complete the accurate segmentation of lung parenchyma.Second,Segmentation of pulmonary nodules based on aggregated Unet networks.In this paper,based on the characteristics of nodule images,a kind of aggregated Unet network is proposed,which can better integrate the shallow edge,color,texture and other deep abstract features.This paper uses 888 Lunal 6 lung CT dataset to verify.The experimental results show that the aggregated Unet network for lung nodules segment,its segmentation accuracy and training time are better than Unet and Segnet structures.Third,Classification of lung nodules based on transfer learning.For medical image datasets,it is difficult to train an effective depth network that can accurately extract nodule features on a limited data set.Based on the transfer learning method,this paper fine-tunes the convolutional neural network model parameters trained under a large number of natural image data sets,and uses the support vector machine to complete the two classifications to reduce false positives.The experimental results show that the algorithm based on transfer learning and support vector machine can accurately classify lung nodules.