Graph attention autoencoder inspired CNN based brain tumor classification using MRI.

• We propose GATE-CNN architecture for binary classification of brain tumor using MR (Magnetic Resonance) images. • We have implemented our model on three different datasets; all consists of MRI with a significant difference in number of images. • The first dataset consists of tumorous and non-tumorous MRIs, second consists of MRIs with glioma and pitutiary (types of brain tumor), and the third consists of cancerous and non-cancerous (severity of brain tumor) images. • For all three datasets we found better classification results than any of the existing state-of-the-art models. Early and accurate detection is a solitary precaution to overcome the brain tumor. Otherwise, it will result in a deadly disease. Brain tumor (BT) detection using magnetic resonance is an essential and challenging job in the medical domain. To combat the aggressive spreading of BT, clinical imaging (MRI and X-Ray) can be an appropriate method for diagnosis. In this paper, we applied attention based image classification, which is a new paradigm for obtaining improved accuracy than the state-of-the-art models. We propose a GATE-CNN (Graph Attention AutoEncoder-Convolution Neural Network) model in this work for the classification of BT. We calculate the attention values of the neighboring pixels on each and every pixel present in the graph then process the graph using GATE framework and the processed graph with attention values is then passed to CNN framework for generation of final output. Further, we apply Adamax optimizer to optimize the training hyperparameters of CNN. We perform the BT classification using the proposed method on three different datasets among benign and malignant BT for first dataset, glioma and pituitary for second dataset, and normal and abnormal BT images for third dataset; all the three datasets consists of MRI images. We then compare the proposed model with a variety of CNN (Deep-CNN, Multi-Input CNN, and basic CNN) models dealing with several types of medical imaging (thermal image, MRI, X-Ray, etc.). Our model results in the accuracy of 98.27% for first dataset, 99.83% for second dataset, and 98.78% for third dataset demonstrating preferable network performance than the already established state-of-the-art CNN models. [ABSTRACT FROM AUTHOR]