Magnetic Resonance Imaging (MRI) technology has been widely applied to generate high-resolution images for brain tumor diagnosis. However, manual image reading is very time and labor consuming. Instead, automatic tumor detection based on deep learning models has emerged recently. Although existing models could well detect brain tumors from MR images, they seldom distinguished primary intracranial tumors from secondary ones. Therefore, in this paper, we propose an attention guided deep Convolution Neural Network (CNN) model for brain tumor diagnosis. Experimental results show that our model could effectively detect tumors from brain MR images with 99.18% average accuracy, and distinguish the primary and secondary intracranial tumors with 83.38% average accuracy, both under ten-fold cross-validation. Our model, outperforming existing works, is competitive to medical experts on brain tumor diagnosis.