A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

• Published in: Healthcare (Basel) Vol. 9; no. 2; p. 153
• Main Authors: Díaz-Pernas, Francisco Javier, Martínez-Zarzuela, Mario, Antón-Rodríguez, Míriam, González-Ortega, David
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 02.02.2021; MDPI AG
• Subjects: brain tumor classification; convolutional neural network; data augmentation; deep learning; MRI; multiscale processing; deep learning; data augmentation; multiscale processing; MRI; brain tumor classification; convolutional neural network
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare9020153

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.