Brain Tumor Detection by using Artificial Neural Network

Brain tumor is one of the most dangerous diseases which require early and accurately detection methods, now most detection and diagnosis methods depend on decision of neurospecialists, and radiologist for image evaluation which possible to human errors and time consuming. This study reviews and describe the processes and techniques used in detection brain tumor based on magnetic resonance imaging (MRI) and artificial neural networks (ANN) techniques, Which executed in the different steps of Computer Aided Detection System (CAD) after collected the image data (MRI); first stage is pre-processing and post-processing of MRI images to enhancement it and make it more suitable to analysis then used threshold to segment the MRI images by applied mean gray level method. In the second stage was used the statistical feature analysis to extract features from images; the features computed from equations of Haralick's features based on the spatial gray level dependency matrix (SGLD) of images. Then selected the suitable and best features to detect the tumor localization. In the third stage the artificial neural networks were designed; the feedforward back propagation neural network with supervised learning were apply as automatic method to classify the images under investigation into tumor or none tumor. the network performances was evaluated successfully tested and achieved the best results with accuracy of 99%, and sensitivity 97.9%.