A Deep Learning Approach for Brain Tumor Classification and Segmentation
  Using a Multiscale Convolutional Neural Network
Francisco Javier Díaz-Pernas Mario Martínez-Zarzuela Míriam Antón-Rodríguez David González-Ortega
In this paper, we present a fully automatic brain tumor segmentation and
classification model using a Deep Convolutional Neural Network that includes a
multiscale approach. One of the differences of our proposal with respect to
previous works is that input images are processed in three spatial scales along
different processing pathways. This mechanism is inspired in the inherent
operation of the Human Visual System. The proposed neural model can analyze MRI
images containing three types of tumors: meningioma, glioma, and pituitary
tumor, over sagittal, coronal, and axial views and does not need preprocessing
of input images to remove skull or vertebral column parts in advance. The
performance of our method on a publicly available MRI image dataset of 3064
slices from 233 patients is compared with previously classical machine learning
and deep learning published methods. In the comparison, our method remarkably
obtained a tumor classification accuracy of 0.973, higher than the other
approaches using the same database.