 We have proposed an integrated algorithm to classify brain tumors following two stages.  We have used the Kernel Support Vector Machine (KSVM) classifier.  The linear kernel achieved 97.5% accuracy and 98.57% accuracy in the first and second classifiers. The studies on brain tumor detection and classification are continuing to improve the specialists' ability in diagnosis. Magnetic Resonance Imaging (MRI) is one of the most common techniques used to evaluate brain tumors diagnosis. However, brain tumors diagnosis is a difficult process due to congenital malformations and possible errors in diagnosing benign from malignant tumors. Therefore, this research aims to propose an integrated algorithm to classify brain tumors following two stages using the Kernel Support Vector Machine (KSVM) classifier. First stage classifies the tumors as normal and abnormal, and the second classifies abnormal tumors as benign and malignant. The first KSVM employs extraction features by considering the pixel values to classify images as a shape. In contrast, the second KSVM uses the Discrete Wavelet Transform (DWT), followed by the Principal Component Analysis (PCA) technique to extract and reduce features and improve the model performance. Also, K-means clustering algorithm is used to segment, isolate and calculate the tumor area. The KSVM classifiers use two kernels (linear and Radial Basis Function (RBF)).Obtained results showed that the linear kernel achieved 97.5% accuracy and 98.57% accuracy in the first and second classifier, respectively. For all linear classifiers, a 100% sensitivity level is achieved. This work validates the proposed model based on the (K-fold) strategy.