  In recent advancement towards computer based diagnostics system, the
classification of brain tumor images is a challenging task. This paper mainly
focuses on elevating the classification accuracy of brain tumor images with
transfer learning based deep neural network. The classification approach is
started with the image augmentation operation including rotation, zoom,
hori-zontal flip, width shift, height shift, and shear to increase the
diversity in image datasets. Then the general features of the input brain tumor
images are extracted based on a pre-trained transfer learning method comprised
of Inception-v3. Fi-nally, the deep neural network with 4 customized layers is
employed for classi-fying the brain tumors in most frequent brain tumor types
as meningioma, glioma, and pituitary. The proposed model acquires an effective
performance with an overall accuracy of 96.25% which is much improved than some
existing multi-classification methods. Whereas, the fine-tuning of
hyper-parameters and inclusion of customized DNN with the Inception-v3 model
results in an im-provement of the classification accuracy.
