  In the clinical diagnosis and treatment of brain tumors, manual image reading
consumes a lot of energy and time. In recent years, the automatic tumor
classification technology based on deep learning has entered people's field of
vision. Brain tumors can be divided into primary and secondary intracranial
tumors according to their source. However, to our best knowledge, most existing
research on brain tumors are limited to primary intracranial tumor images and
cannot classify the source of the tumor. In order to solve the task of tumor
source type classification, we analyze the existing technology and propose an
attention guided deep convolution neural network (CNN) model. Meanwhile, the
method proposed in this paper also effectively improves the accuracy of
classifying the presence or absence of tumor. For the brain MR dataset, our
method can achieve the average accuracy of 99.18% under ten-fold
cross-validation for identifying the presence or absence of tumor, and 83.38%
for classifying the source of tumor. Experimental results show that our method
is consistent with the method of medical experts. It can assist doctors in
achieving efficient clinical diagnosis of brain tumors.
