A combining two ksvm classifiers based on True pixel values and Discrete wavelet transform for mri-based brain tumor detection and Classification

Abstract

The studies on brain tumor detection and classification are continuing to improvethe specialists’ ability in diagnosis. Magnetic Resonance Imaging (MRI) is one ofthe most common techniques used to evaluate brain tumors diagnosis. However,brain tumors diagnosis is a difficult process due to congenital malformations andpossible errors in diagnosing benign from malignant tumors. Therefore, thisresearch aims to propose an integrated algorithm to classify brain tumors followingtwo stages using the Kernel Support Vector Machine (KSVM) classifier. Firststage classifies the tumors as normal and abnormal, and the second classifiesabnormal tumors as benign and malignant. The first KSVM employs extractionfeatures by considering the pixel values to classify images as a shape. In contrast,the second KSVM uses the Discrete Wavelet Transform (DWT), followed by thePrincipal Component Analysis (PCA) technique to extract and reduce features andimprove the model performance. Also, K-means clustering algorithm is used tosegment, isolate and calculate the tumor area. The KSVM classifiers use twokernels (linear and Radial Basis Function (RBF)). Obtained results showed thatthe linear kernel achieved 97.5% accuracy and 98.57% accuracy in the first andsecond classifier, respectively. For all linear classifiers, a 100% sensitivity level isachieved. This work validates the proposed model based on the (K-fold) strategy