Wavelet Transform to Improve Accuracy of a Prediction Model for Overall Survival Time of Brain Tumor Patients Based On MRI Images

Abstract

In this poster, denoising wavelet transform (DWT) method is proposed to improve the accuracy of a prediction model for overall survival time of brain tumor patients using Magnetic resonance imaging (MRI) images based on classification approach. The BraTS dataset is used in this work. The histogram features are extracted from MRI images to train a prediction model using machine learning methods. As the dataset consists of only 163 samples, various machine learning methods have been used to develop an accurate prediction model. In general the MRI imaging system corrupted the MRI information with noise. The results show that the two-dimension denoising wavelet transform method slightly improved the accuracy of a prediction model based on histogram features. The best accuracy is achieved by daubechies 4 level 4 (db4-L4) with a 10 folds cross validation linear support vector Machine (SVM) when including patients' age information. However, daubechies 2 level 1 and 3 (db2-L1, db2-L3) with a 10 folds cross validation simple tree produce an improved accuracy when the patients' age does not combined with histogram features vector. When a 10% hold out validation method is used, the daubechies 2 level 3 (db2-L3) with simple tree achieves 66.7% accuracy.