Combining tissue segmentation and neural network for brain tumor detection

The decisive plan in a large number of image processing applications is to take out the significant features from image data, in which a description, interpretation, or understanding of the scene can be provided by the machine. The segmentation of brain tumor from Magnetic Resonance (MR) images is a vital, but time-consuming task performed by medical experts. In this paper, we have presented an effective brain tumor detection technique based on Neural Network (NN) and our previously designed brain tissue segmentation. This technique hits the target with the aid of the following major steps, which includes: Pre-processing of the brain images., segmentation of pathological tissues (Tumor), normal tissues (White Matter (WM) and Gray Matter (GM)) and fluid (Cerebrospinal Fluid (CSF)), extraction of the relevant features from each segmented tissues and classification of the tumor images with NN. As well, the experimental results and analysis is evaluated by means of Quality Rate (QR) with normal and the abnormal Magnetic Resonance Imaging (MRI) images. The performance of the proposed technique is been validated and compared with the standard evaluation metrics such as sensitivity, specificity and accuracy values for NN, K-NN classification and bayesian classification techniques. The obtained results depicts that the classification results yields better results in NNs when compared with the other techniques.