Detection of Brain Tumor based on Features Fusion and Machine Learning

Abstract

Automated detection of brain tumor is a more challenging work due to the variability and complexity of shape, size, texture and location of lesions. The non-invasive MRI methods appear as a front line brain tumor detection tools (without ionization radiation). In this manuscript, an unsupervised clustering approach for tumor segmentation is proposed. Moreover, a fused feature vector is used which is a mixture of Gabor wavelet features (GWF), histograms of oriented gradient (HOG), local binary pattern (LBP) and segmentation based fractal texture analysis (SFTA) features. Random forest (RF) classifier is applied for classification between three sub tumoral regions such as complete, enhancing and non-enhancing tumor. To avoid over fitting problem, fivefold and 0.5 holdout cross-validation approaches are used. The promising detection efficiency depicts the dominance of proposed approach.