Brain Cancer classification Based on Features and Artificial Neural Network

Abstract: MRI (Magnetic resonance Imaging) brain tumor images Classification is a difficult task due to the variance and complexity of tumors. This paper proposed techniques to classify the MR human brain images. The proposed classification technique consists of three stages, namely, pre-processing, feature extraction and selection, and classification. Features are extracted by using the gray level co-occurrence matrices and the gray level run length matrices (GLCM & GLRLM), 18 features were determined from the image, then selected the most important features that saved to the database. In the final stage, the classifier based on probabilistic neural network (PNN) have been used to classify MRI brain images, the proposed algorithm is trained with 50 images of (Sarcoma, Anaplastic Astrocytoma, Meningioma, and Benign) and tested with 65 images. The accuracy of this method was up to 98%.

Keywords: MRI, brain tumor, classification, image processing, PNN.