Classification Of Lung Cancer Images Based On Convolutional Neural Network

Lung cancer is the most common cancer and the main cause of cancer death,and is a malignant disease with poor prognosis.The average 5-year survival rate of patients is less than 20%.Therefore,it is important to detect lung lesions early to increase the chance of treatment and increase the patient survival.However,due to the subtle differences between benign and malignant lung nodules,even for human experts,the diagnosis of lung cancer is a difficult task.Therefore,a computer-aided diagnosis system needs to be developed to assist doctors in diagnosing lung cancer nodules and provide their diagnostic accuracy.Based on CT images of lung cancer,this paper focuses on the classification of lung cancer images,so as to help doctors diagnose and treat patients with early lung cancer and improve the chances of cure.Firstly,this paper introduces the research status of deep learning at home and abroad and the research status of lung cancer image classification methods.Then,the typical CNN model of lung cancer image classification was analyzed,the characteristics of each mainstream CNN model were analyzed,and the performance of Alex Net,ZFNet,Google Net and VGGNet were compared.Experiments showed that,based on the original image,VGG16 has a higher classification accuracy than other network structure models.On this basis,lung cancer images were classified based on VGG16 convolutional neural network,public lung cancer data set LIDC-IDRI was selected,lung cancer images were preprocessed,activation functions were selected and model parameters were set through experiments,and accuracy,sensitivity,specificity and ROC curves were selected as indicators for comparative analysis with other paper methods.The experiment showed that the accuracy and sensitivity of lung cancer image classification based on VGG16 had a small increase,but there were still problems such as long training time.Aiming at the problem of too many parameters and too many layers in VGG16 network,an optimized network structure was proposed toreduce the model parameters,accelerate the network convergence speed and reduce the over-fitting problem of the model.Through the training and testing on the LIDC-IDRI data set,the results show that the proposed optimized convolutional neural network can extract lung cancer nodule information from 3d images and effectively use the information to classify lung cancer images.