Automated Primary Brain Tumors Classification of MR Images using Texture Features Extraction and Machine Learning
Keywords:
Brain Tumor, Magnetic Resonance Imaging, Texture Features, Normalization, Classification.Abstract
Among all cancers, brain cancer has the greatest fatality rates due to its difficult detection and misdiagnosis. The ability to distinguish on the basis of variations in texture, shape, intensity, and deep features, makes Magnetic Resonance Imaging (MRI) a popular tool for the diagnosis of brain cancer. These features have been utilized during the classification of brain tumors. This paper proposes a methodology for classifying primary brain tumors in binary class using the texture features of Magnetic Resonance (MR) images. The Gabor 2D filter, Haralick Texture Features (HTF), Edge Continuity Texture Features (ECTF), First-Order Statistical Texture Features (FOSTF), Local Binary Pattern Texture Features (LBPTF), Difference Theoretic Texture Features (DTTF), and Spectral Texture Features (STF) are used for texture feature extraction of brain MR images. The Tanh normalization method further normalized the retrieved features to deal with outliers and dominant features. The Infinite Latent Feature Selection (ILFS) approach is used for feature ranking to find the most pertinent and non-redundant texture features once the features have been normalized. The top 200-ranked features are used for the classification. The proposed method is compared with different supervised classifiers and achieves high classification accuracy with a minimum error rate and better precision, sensitivity, specificity, Mathew Correction Coefficient (MCC), and F1-Score values. An accuracy of 99.4% has been achieved with the cubic Support Vector Machine (SVM) classifier for the Figshare dataset. A comparison has also been made between the proposed method and other state-of-the-art methods.
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