Life Cycle Analysis for Brain Tumor Patients Based on Adaboost

With the increasing incidence of brain tumors in modern society, the analysis of the survival cycle of patients with brain tumors has become increasingly significant in clinical practice. In order to solve the problem of low accuracy of the current method, this paper proposes a life cycle analysis system for brain tumor patients based on Adaboost. Firstly, preprocessed magnetic resonance images and obtained its region of interest (ROI) and segmentation part, then extracted the texture features of multi-sequence MR for brain tumor patients, and performed feature selection using mutual information as the evaluation standard to obtain feature subset. Finally, this article builds an analysis model with Adaboost.R2 as the core method, and uses the feature subset to complete the training and tuning of the analysis model to complete the analysis of the survival period of tumor patients. The cross-validation experimental results on Brats2018 training data confirm that the analysis accuracy of this system is better than the Top3 methods of Brats2018 challenge and the traditional regression analysis methods.