Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning

Received: 3 March 2020 Accepted: 23 November 2020 This paper presents an efficient and accurate automated system based on the hybrid XGBoost with Random forest (XGBRF) ensemble model in order to classify the Glioma (type of mostly diagnosed brain tumor) into low grade and high grade Glioma. In this approach initially global thresholding is employed on various MRI sequence and their fusion combinations in order to perform the accurate segmentation. Then uses a proposed Enhanced wavelet binary pattern run length matrix method (EWBPRL) for textural features extraction from the region of interest or segmented Glioma tumor region. This proposed feature extraction method is based on the Discrete wavelet transform (DWT), Local Binary pattern (LBP) and Gray level run length Matrix (GLRLM) methods to extract texture features from the segmented region. Some morphological features are also computed from the segmented region along the textural features. Finally both these extracted features are employed in order to train a hybrid XGBoost with Random forest ensemble model for the first time. The proposed automated system apart from accurately detecting and segmenting the tumors region from the fused MRI sequences, also tends to determine the grading of Glioma in terms of severity. The proposed system is evaluated on the large size balance local dataset and as well as on the popular global datasets like BRATS 2013 and BRATS 2015. This approach offers an encouraging accuracy of 99.25% on the local dataset with the fusion of T1C+T2+Flair MRI sequence as compare to 96.75% accuracy, which is achieved utilizing the fusion of T1+T1C+T2+Flair MRI sequence.