Binary glioma grading framework employing locality preserving projections and Gaussian radial basis function support vector machine. Issue 4 (8th June 2021) - Singh, Rahul; Goel, Aditya; Raghuvanshi, Deepak Kumar
Abstract: Gliomas are one of the most dangerous brain tumours, which are life‐threatening in their highest grade. Manual diagnosis and detection of brain tumours are time‐consuming. Thus, early and accurate detection of such tumours becomes essential for its prognosis. In this work, we aim to develop a fully automated glioma grade classification framework. The proposed method aims to learn relevant and non‐redundant features from magnetic resonance brain tumour images. Gabor filter banks extract discriminant features from the brain images, and locality preserving projections are utilized for feature reduction to achieve non‐redundant features. We train Gaussian radial basis function–support vector machine classifiers using these features for classifying gliomas into low grade glioma (LGG) or high‐grade glioma. We apply Synthetic Minority Over‐Sampling Technique on the minority class (LGG) to mitigate the class imbalance problem. The performance of the proposed method is validated by conducting fivefold cross‐validation. We evaluate the performance of our proposed framework on different BRATS datasets: BRATS 2013, BRATS 2015, and BRATS 2017. We perform extensive experiments on each dataset separately, and experimental findings demonstrate that our proposed approach provides significantly superior performance compared to state‐of‐the‐art techniques.