Lung Nodule Classification Model With Fusion Of Dual Path Network And Capsule Network

Lung cancer is one of the most common malignant tumors in the world and has a high rate of incidence and mortality.Continuous increase of lung cancer is related to the surrounding environment as well as habits.With industrial growth and urbanization,food,water,soil and air pollution levels are aggravated.Especially in northern China,air pollution is more serious.These factors lead to increase in respiratory diseases and may cause lung cancer in the long run.The long term harmful effects of the surrounding can be prevented by early screening and diagnosis.However,many lung cancer patients do not exhibit such symptoms in early stages which leads to the cancer not being detected on time.By the time clinical symptoms or serious conditions appear and the patient visits a hospital for medical examination,the cancer usually progresses to a late stage.Studies reveal that 80% of the patients were in the late stage of lung cancer or local lung cancer by the time they were diagnosed and had lost the best opportunity for treatment.Therefore,it is of great significance and practical value for the early diagnosis and treatment of lung diseases that the pulmonary nodules be identified and classified automatically as benign or malignant.Based on the lung CT images in medical CT images for classification and prediction of lung cancer diseases,this paper proposes a Dual Path Network and Capsule Network benign and malignant prediction model of lung nodules-DPNCapsNet.First,window setting,normalization processing,and lung parenchyma extraction are performed for general CT image data.Subsequently,corrosion and dilation are used to remove irrelevant tissues and preserve nodules and excessive interference in CT images is eliminated to enhance model feature mining and analysis capabilities.Then,the lung nodules are classified into benign and malignant tags by collating the nodule development degree marked by the doctor in the LIDC-IDRI data,and the CT image coordinate system converted to the pixel coordinate system.The position of the nodule is segmented and extracted in a size of 48 × 48 × 48 cube,plus a 48 × 48 × 48 cube area around the random location given in LUNA16 as training input.Finally,the preprocessed training data is enhanced into the DPNCapsNet model for training using the ZCA whitening algorithm.The model is mainly composed of three parts.First,the cube data is passed through the DPN network.The DPN block consisting of a convolution network,ResNet(Residual Network)and DenseNet(Dense Connection Network)continuously extracts data features.Then,the features extracted by the DPN network are directly collected using the capsule network,and a vector capsule of a fixed length is constructed by a convolution operation at the primary capsule layer.Finally,in the output capsule layer,a dynamic routing algorithm is used to gradually route the feature capsules to benign and malignant feature classes to obtain a fixed-length classification capsule.The output vector of the capsule contains different feature information of the nodules and the nodule length of the classification capsule.Probability representing benign and malignant classifications.In this paper,the LUNA16 data set is used as the main method,and the LIDC-IDRI data set is used as an auxiliary method to extract training data and doctor labeling.The data set is fully preprocessed to reduce redundant interference and improve training efficiency.Combining the advantages of Dual Path Network with fast feature extraction and high accuracy,and the capsule network’s sensitivity to spatial attitude and orientation information,a lung nodule classification prediction model is constructed which can obtain stable results with less training data and times.The experimental results show that in the LUNA16 data set,the classification prediction effect for benign and malignant nodules reaches 91.56%,which is close to the level of professional doctors.