Support Vector Machine, Naive Bayes, and Artificial Neural Network Back Propagation Comparison in Detecting Brain Tumor

Abstract

Brain tumors are abnormal tissue that grow uncontrolled and affect a patient's neurological function. Brain tumors come in different shapes and characteristics. Moreover, its location also differs for each patient. Brain tumors can be detected using machine learning algorithms using magnetic resonance imaging (MRI) images. However, a different machine-learning comparison is limited and needs further investigation. This study aims to compare three machine-learning methods, i.e., Support Vector Machine (SVM), Naive Bayes (NB), and Artificial Neural Network Back Propagation (ANN-BP) algorithms for detecting brain tumors. Before the comparison started, MRI image quality was enhanced by performing denoising, histogram equalization, and thresholding. After that, Gray Level Co-occurrence Matrix feature extraction was performed. MRI brain images in JPEG format were acquired from an open-access database. One thousand brain tumor and 1000 normal tumor images are used as the training data, while 100 brain tumor and 100 normal tumor images are used as testing data. Each algorithm's accuracy, precision, sensitivity, and Matthews Correlation Coefficient (MCC) are evaluated and reported. The study showed that the SVM algorithm acquired the highest performance in detecting brain tumors, followed by ANN-BP and NB. The highest accuracy, precision, sensitivity, and MCC values for testing in SVM were 98,75%, 98,22%, 99,30%, and 0,9751, respectively. Meanwhile, in testing, the highest accuracy, precision, sensitivity, and MCC values were 90.50%, 98.80%, 82.00%, and 0.8220, respectively. In conclusion, this study showed the superiority of the SVM algorithm in detecting brain tumor compared to ANN-BP and NB by performing image enhancement steps and GLCM feature extraction before its detection.