Classification Of Benign And Malignant Solitary Pulmonary Nodules Based On CNN And SVM

The classification of benign and malignant solitary pulmonary nodules is an important part of medical image processing.Based on the complex image features of pulmonary nodules,the challenge of traditional artificial diagnosis and the existence of uncertain factors in the recognition of pulmonary nodules,it is necessary to give full play to the auxiliary effect of machine learning in diagnosis and realize complementary advantages.In view of this,this paper introduces the deep neural network,applies the convolution neural network model to the classification of benign and malignant solitary pulmonary nodules,and makes full use of its characteristics such as stronger learning ability than the traditional method,more convenient learning method and lower image features,so as to reduce the number of feature extraction of pulmonary nodules in CT images,and make full use of the advantages of strong generalization ability of support vector machine The accuracy of recognition of benign and malignant pulmonary nodules in small samples of high altitude area.First of all,this paper analyzes the current situation of the classification of benign and malignant solitary pulmonary nodules,and studies the effect of deep neural network and support vector machine on the classification of pulmonary nodules.Secondly,the principle of convolution neural network is introduced in detail,the advantages and disadvantages of convolution neural network are compared,the process of extracting solitary pulmonary nodules by convolution neural network is explored,the over fitting of convolution neural network is improved by batch normalization,and the gradient method of automatic differential calculation is introduced to improve the accuracy of classification of benign andmalignant pulmonary nodules.Thirdly,this paper studies the image classification algorithm based on the support vector machine,using the strong generalization ability of the support vector machine for small samples,improving and applying the cuckoo search algorithm,using its strong optimization ability to find the relevant parameters of the support vector machine,effectively improving the classification effect of the SVM for regional pulmonary nodules.Finally,convolution neural network and support vector machine algorithm are used to classify the benign and malignant solitary pulmonary nodules,and compared with the previous methods.The experimental results show that the proposed method improves the specificity,sensitivity and accuracy in the classification of benign and malignant solitary pulmonary nodules with small regional samples.