Brain tumor diagnosis using MRI is an active area of research and the traditional methods of brain tumor classification lack the accurate identification of the tumor region, which demands an effective classification strategy for brain tumor identification. Accordingly, an effective classifier, named as Chaotic Biogeography-based Ride-Neural Network (CBB Ride-NN) is proposed for brain tumor detection using MRI. Initially, the brain tumor segmentation is processed using the active contour model, where the tumor and the non-tumor regions are differentiated from the brain images in order to extract the texture features for classification. Thus, for classification of brain tumors, proposed Chaotic Biogeography-based Ride-Neural Network is employed, where the proposed CBB Ride optimization algorithm trains the neural network. The classification accuracy of the brain tumor classification is effective and addresses the issues of the existing classifiers. The effectiveness of the proposed CBB Ride-NN for tumor classification is revealed using the analysis with the BRATS dataset with respect to the performance metrics, namely accuracy, specificity, and sensitivity. The measures, like accuracy, sensitivity, and specificity acquired the maximal values of 0.9327, 0.9288, and 0.9226, respectively. © 2020, Institute of Advanced Scientific Research, Inc.. All rights reserved.