Multiclass classification of central nervous system brain tumor types based on proposed hybrid texture feature extraction methods and ensemble learning.

This paper presents an automated approach to perform multiclass classification of four majorly diagnosed Central nervous system brain tumors. The Astrocytoma, Glioblastoma multiforme, Meningioma and Oligodendroglioma are the four types of central nervous system brain tumors types, whose classification is being performed with the aid of this proposed approach. In addition, this proposed approach also used to perform binary classification of Glioma brain tumors into low grade and high grade Glioma tumor. The proposed automated approach for multiclass and binary class classification is based on the threshold segmentation of fused Magnetic Resonance Imaging sequences, proposed hybrid feature extraction methods along with shape based features and ensemble learning classifier. The two hybrid feature extraction methods are proposed in this paper, one based on the Discrete wavelet transform + Gradient Grey level co-occurrences matrix and second one based on the Discrete wavelet transform + Local binary pattern + Grey level run length matrix. The extracted texture features along with the shape based features are further reduced employing Principal component analysis. The resulted selected features are finally used to train the majority voting based ensemble classifier model with the aid of Central nervous system local dataset, Brain Tumor Segmentation 2013 and 2015 global dataset. The proposed automated system delivers an accuracies of 99.12, 95.24, 97.62 and 97.62 for the correct classification of Astrocytoma, Glioblastoma multiforme, Meningioma and Oligodendroglioma over the Central nervous system local dataset. Whereas delivers an accuracy of 100 and 99.52 for the binary classification of Glioma on the Brain Tumor Segmentation 2013 and 2015 global datasets employing 10-fold cross validation. [ABSTRACT FROM AUTHOR]