MR brain tumor classification is one of the extensively utilized approaches in medical prognosis. However, analyzing and processing MR brain images is still quite a task for radiologists. To encounter this problem, the evaluation of existing canonical techniques has already been done. There are numeral MR brain tumor classification approaches that are being used for medical diagnosis. In this paper, we have developed an automated computer-aided network for diagnosis of MR brain tumor class, i.e., HGG and LGG. We have proffered a Gabor-modulated convolutional filter-based classifier for brain tumor classification. The inclusion of Gabor filter dynamics endows the competency to deal with spatial and orientational transformations. This mere modification (modulation) of conventional convolutional filters by Gabor filters empowers the proposed architecture to learn relatively smaller feature maps and thereby, decreasing network parameter requirement. We have introduced some skip connections to our modulated CNN architecture without introducing an extra network parameter. Pre-trained networks, i.e., Alex-Net, Google-Net (Inception V1), Res-Net and VGG 19 have been considered for performance evaluation of our proposed Gabor-modulated CNN. Additionally, some popular machine learning classification techniques have also been considered for comparative analysis. Experimental findings demonstrate that our proposed network has limited network parameters to learn; therefore, it is quite easy to train such networks.
