Artificial neural networks design for classification of brain tumour

In this system, we exploit the capability of Back propagation neural network (BPN) and Radial Basis Function Neural network (RBFN) to classify brain MRI images to either cancerous or noncancerous tumour automatically. It is classified with respective to symmetry of brain image, exhibited in the axial and coronal images. The initial objective of this study was not to discover which algorithm is superior in classification tasks, but to examine the advantages and downfalls of each algorithm under varying conditions. Using the optimal texture features extracted from normal and tumor regions of MRI by using statistical features, BPN and RBF classifiers are used to classify and segment the tumor portion in abnormal images. Both the testing and training phase gives the percentage of accuracy on each parameter in neural networks, which gives the idea to choose the best one to be used in further works. The results showed outperformance of RBFN algorithm when compared to BPN with classification accuracy of 85.71% which works as promising tool for classification and requires extension in brain tumour analysis.