Brain cancer classification using elman recurrent neural network with genetic algorithm optimization

Abstract

Elman Recurrent Neural Network (ERNN) is a model that accommodates network output to be network input to produce the next network output. Genetic algorithms are computational approaches to solving problems that are modeled after the process of biological evolution. The purpose of this research is to classify brain cancer from Magnetic Resonance Imaging (MRI) brain with ERNN model. To improve the accuracy of the classification results, the ERNN model parameters are optimized using genetic algorithms. The data used in the model is the result of extracting MRI images using Gray Level Co-occurrence Matrix (GLCM) method, which consists of 13 variables, those are contrast, correlation, energy, homogeneity, entropy, sum of square variance, Inverse Difference Moment (IDM), sum average, sum entropy, sum variance, difference entropy, maximum probability, and dissimilarity. Classification results are evaluated using accuracy, sensitivity, and specificity on training and testing data. The study has perfect results on training data with no misclassification, while on the testing data the results are also very satisfying with sensitivity, specificity, and accuracy values are 100%, 93.33%, 96.43%.