Tumor classification becomes an essential requirement in the proper diagnosis and treatment approach of brain tumors. The present paper discusses an advanced implementation by combining the technique of UNet segmentation with Deep Maxout classifier for efficient tumor classification with precision. The convolutional neural network, UNet, would be utilized to perform a pixelwise segmentation that can lead to accurate identification of the region where the tumor is located within the MRI scan. The Deep Maxout classifier would then be implemented in order to classify the type of tumor with a high degree of accuracy. The proposed model trained and tested through publicly available brain tumor datasets was found to outperform state-of-the-art classifiers. It can be well compared with the known segmentation and classification techniques and can be applied to the clinic to aid in diagnosis and the development of personalized treatment plans for patients with a brain tumor.