Hash Retrieval Method For CT Images Of Pulmonary Nodules Based On Deep Learning

Facing the explosive growth of computed tomography(CT)images of lung nodules,it is possible to quickly and accurately diagnose CT images of lung nodules,which has become a very difficult job.The method of retrieving CT images of lung nodules by computer simulation has been widely used in the diagnosis of lung cancer.Using the case data in the lung cancer database that has been diagnosed,through CT image retrieval of lung nodules,it provides a reference for doctors and reduces the chance of misdiagnosis and misjudgment.Therefore,the retrieval of CT images of pulmonary nodules is of great significance in the diagnosis of doctors.Although the traditional retrieval methods have achieved remarkable results,there are still many problems that need to be solved.In view of these problems,the research content of this article is as follows:(1)Aiming at the problem of large errors in CT image retrieval of lung nodules,this article first preprocesses the CT images of lung nodules: mainly including the process of extracting lesions,that is,extracting lung nodules from the CT images of lung.Through experimental comparison,the maximum inter-class variance method,morphological expansion and erosion,and the maximum connected area algorithm method are used to realize the preprocessing of CT images of pulmonary nodules.The experimental results are significant.(2)To solve the problem of deep data mining of CT image features of lung nodules,this paper uses deep learning to retrieve CT images of lung nodules: mainly based on Deep Belief Network(DBNs)and Iterative quantization,ITQ)to build an unsupervised hash retrieval model of CT images of lung nodules.The experimental results show that this method has obvious effects on improving the precision and recall rates.(3)Aiming at the insufficient amount of data in the CT image database of lungnodules,this paper uses transfer learning to retrieve lung nodule CT images,mainly using supervised learning hash retrieval methods: based on Alex Net convolutional neural network(CNNs)model To build a network model for transfer learning,construct a hash function similar to the Locally Sensitive Hash(LSH)to encode the image binary,and then use the similarity measure of hierarchical retrieval to retrieve CT images of lung nodules.Finally,the supervised learning hash retrieval and classification of CT images of lung nodules can be realized,and the superiority of this method is demonstrated through multiple comparative experiments.Finally,based on the LIDC-IDRI lung nodule data set,the retrieval performance of this method is evaluated.The experimental results show that the two deep learning-based image retrieval methods proposed in this paper have better image feature extraction and retrieval capabilities,and further verify The effectiveness of CT image retrieval in pulmonary nodules was demonstrated.