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Application of machine learning in multiclass classification of brain tumor types has contributed to the development of computer aided diagnosis (CAD) system that can potentially enhance accuracy and speed up diagnosis of the disease. LDA+ELM model with different activation functions were investigated to achieve the optimum performances in terms of accuracy, Kappa statistic, sensitivity, precision, F-measure, training time and test time. We also proposed a user-friendly GUI in characterizing brain tumor types using MR images. First, a total of 3064 slices of CE T1-weighted brain MR images with ground truth were downloaded from a free online database. The manually segmented tumor region was augmented and then undergo several feature extraction techniques. All the feature descriptors obtained were then concatenated, followed by LDA dimensionality approach. Performance of different number of LDA features and ELM activation functions were investigated by repeated training and test. The ELM output of training data for each class was used to fit GMM and these probabilistic models used to estimate posterior probabilities of test data. LDA+ELM model with 5 LDA feature input, utilizing sigmoid function as hidden nodes activation functions achieves the best generalization performance with accuracy of 98.92% and corresponding F-scores for meningioma, glioma and pituitary tumor of 97.81%, 99.1% and 99.5% respectively. The proposed method (LDA+ELM) model performs better compared to other previous works using the same dataset and performing the same classification task.

Effective and Efficient LDA+ELM Model for Supervised Classification of Brain Tumor Types Using 2D MRI Scans