Machine Learning and Deep Learning Techniques to Predict Overall Survival of Brain Tumor Patients Using MRI Images
2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)
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This paper presents a method to automatically predict the survival rate of patients with a glioma brain tumor by classifying the patients MRI image using machine learning (ML) methods. The dataset used in this study is BraTS 2017, which provides 163 samples; each sample has four sequences of MRI brain images, the overall survival time in days, and the patients age. The dataset is labeled into three classes of survivors: short-term, mid-term, and long-term. To improve the prediction results, various types of features were extracted and trained by various ML methods. Features considered included volumetric, statistical and intensity texture, histograms and deep features; ML techniques employed included support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant, tree, ensemble and logistic regression. The best prediction accuracy based on classification is achieved by using deep learning features extracted by a pre-trained convolutional neural network (CNN) and was trained by a linear discriminant.
Convolutional neural network; Deep learning features; Discrete wavelet transform; Gliomagray level co-occurrence matrix; Intensity texture features; K-nearest neighbors; Logistic regression; Pre-trained CNN; Statistical texture features; Support vector machine; Volumetric features
Biomedical Engineering and Bioengineering
Machine Learning and Deep Learning Techniques to Predict Overall Survival of Brain Tumor Patients Using MRI Images.
2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), 2017