Accurate Classification of Brain Gliomas by Discriminate Dictionary Learning based on Projective Dictionary Pair Learning of Proton Magnetic Resonance Spectra
Adebileje Sikiru Afolabi1, Ghasemi Keyvan, Aiyelabegan Hammed Tanimowo, Saligheh Rad Hamidreza
Proton magnetic resonance spectroscopy, Brain gliomas, dictionary pair learning, sub-dictionary learning
Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of conventional magnetic resonance imaging, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task and the result were compared to the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contains a total of 150
spectra (74 healthy, 23 grade II, 23 grade III, 30 grade IV) from two databases. The datasets from both databases were first coupled together,followed by column normalization. The Kennard-Stone algorithm were used to split the datasets into its training and test sets.
Performance comparison based on the overall accuracy, sensitivity, specificity and precision were conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method were found to outperform the sub-dictionary learning methods 97.78% compared to 68.89% respectively.