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In the clinical diagnosis and treatment of brain tumors, manual image readingconsumes a lot of energy and time. In recent years, the automatic tumorclassification technology based on deep learning has entered people's field ofvision. Brain tumors can be divided into primary and secondary intracranialtumors according to their source. However, to our best knowledge, most existingresearch on brain tumors are limited to primary intracranial tumor images andcannot classify the source of the tumor. In order to solve the task of tumorsource type classification, we analyze the existing technology and propose anattention guided deep convolution neural network (CNN) model. Meanwhile, themethod proposed in this paper also effectively improves the accuracy ofclassifying the presence or absence of tumor. For the brain MR dataset, ourmethod can achieve the average accuracy of 99.18% under ten-foldcross-validation for identifying the presence or absence of tumor, and 83.38%for classifying the source of tumor. Experimental results show that our methodis consistent with the method of medical experts. It can assist doctors inachieving efficient clinical diagnosis of brain tumors.

Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model