CGHF: A Computational Decision Support System for Glioma Classification Using Hybrid Radiomics- and Stationary Wavelet-Based Features

Brain tumors are the most prominent neurologically malignant cancers with the highest injury and death rates worldwide. Glioma classification is crucial for the prognosis, assessment of prognostication and the planning of clinical guidelines before surgery. Herein, we introduce a novel stationary wavelet-based radiomics approach to classify the grade of glioma more accurately and in a non-invasive manner. The training dataset of Brain Tumor Segmentation (BraTS) Challenge 2018 is used for performance evaluation and calculation is done based on the radiomics features for three different regions of interest. The classifier, Random Forest, is trained on these features and predicted the grade of glioma. At last, the performance is validated by usingfive-fold cross-validation scheme. The state-of-the-art performance is achieved considering metric (Acc, Sens, Spec, Score, MCC, AUC) ≡ (97.54%, 97.62%, 97.33%, 98.3%, 94.12%, 97.48%) with machine learning predictive model Random Forest (RF) for brain tumor patients' classification. Considering the importance of glioma classification for the assessment of prognosis, our approach could be useful in the planning of clinical guidelines prior to surgery.