RT info:eu-repo/semantics/article
T1 A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network
A1 Díaz Pernas, Francisco Javier
A1 Martínez Zarzuela, Mario
A1 Antón Rodríguez, Miriam
A1 González Ortega, David
K1 Brain - Tumors - Diagnosis
K1 Tumors cerebrals
K1 Cerebro - Tumores
K1 Neurology
K1 Machine learning
K1 Artificial intelligence
K1 Neural networks (Computer science)
K1 Redes neuronales (Informática)
K1 Signal processing
K1 Statistics
K1 Estadística
K1 Magnetic resonance
K1 Resonancia Magnética
K1 3205.07 Neurología
K1 1209.03 Análisis de Datos
AB 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
PB MDPI
SN 2227-9032
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/59926
UL https://uvadoc.uva.es/handle/10324/59926
LA eng
NO Healthcare, 2021, Vol. 9, Nº. 2, 153
NO Producción Científica
DS UVaDOC
RD 06-ago-2024