Research And Implementation On Brain Tumor Extraction Based On Deep Belief Network

The high incidence and multiple of brain tumor diseases have affected people's life and aroused great concern for this type of diseases,so the research in this area is of great significance.Meanwhile,computer-aided diagnosis is widely used in the medical field,in which accurate segmentation of medical image brain tumors has become a key step for auxiliary diagnosis,in terms of brain tumor disease diagnosis and clinical trials.The segmentation results provide clinicians and researchers with effective information on tumor size,location and type of lesion.Meanwhile,precise localization of brain tumors and intuitive brain tumor pathological analysis provide a reliable basis for the treatment of brain tumor diseases.In the actual clinical diagnosis process,the brain tumor results are usually manually obtained by the clinician's experience.This method is easily influenced by the subjective judgment and the accuracy of the result is not high.Therefore,we need to propose an automatic and effective brain tumor segmentation method.In recent years,the research and development of various deep learning methods used in the field of medical images have provided us with theoretical and technical support.In this thesis,we firstly introduce the current researches on deep learning in medical image processing,and then propose the key technologies of this thesis based on the basic ideas and classification features of Deep Brief Networks(DBN).Based on this,we propose a brain based on deep trust networks Tumor extraction model.The model consists of three steps: brain image preprocessing,brain tumor segmentation based on deep trust network and brain tumor extraction.The research contents of each part are as follows:1)The data of different data sources and formats need to be preprocessed.The preprocessing mainly includes three aspects: image registration and skull peeling,image denoising and image enhancement,and the related technologies in each aspect are summarized.The preprocessed brain images solve the problem of differences between multiple data sources and different data format images so that the experimental results can be compared horizontally.2)Combined with the excellent classification features of DBN,the classification of brain tumors is transformed into segmentation operations and the preliminary brain tumor extraction results are mapped.3)According to the extracted brain tumor results,different types of results using different post-processing methods to get the final accurate results.On the basis of the model,the clinical MRI image data of West China Hospital and the public data set BRATS are used to verify the experiment.The experimental results show that the proposed method is effective.The results of the extraction were qualitatively and quantitatively analyzed and compared with the papers and experiments in related fields.The results also showed that the model achieved good results for brain tumor extraction.