Brain Tumor Segmentation and Classification Using CNN and SVM

Kong, Kuok Yong (2019) Brain Tumor Segmentation and Classification Using CNN and SVM. Final Year Project (Bachelor), Tunku Abdul Rahman University College.

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Abstract

Brain tumor is a group of abnormal cells in the brain which can be either cancerous (malignant) or non-cancerous (benign). Gliomas are the most common brain tumor that can be categorized into low grade gliomas (LGG) and the high-grade glioma (HGG). Identifying the location as well as the area of the tumor has been a tedious job for radiologists, let alone to classify the tumor into the LGG or the HGG. Hence, in this work, a fully automatic approach is proposed to segment the brain tumor in Magnetic Resonance Imaging (MRI) images and classify it into the LGG or the HGG. A high bias algorithm 3D Convolutional Neural-network (CNN) is employed in this work to segment the brain tumor into edema, necrotic, and enhancing tumor. The segmented tumor is then used to generate the radiomics features and the Support Vector Machine (SVM) is used to classify the LGG and HGG tumor. This work is tested on Brats 2017 and the experiments show promising results with the Dice scores, for segmentation and classification for LGG and HGG, at 0.85 Dice Scores and 0.83 F1-Score, respectively.