The application of deep convolutional neural networks to brain cancer images: A survey

Amin Zadeh Shirazi, Eric Fornaciari, Mark D. McDonnell, Mahdi Yaghoobi, Yesenia Cevallos, Luis Tello-Oquendo, Deysi Inca, Guillermo A. Gomez

Producción científica: Contribución a una revistaArtículo de revisiónrevisión exhaustiva

28 Citas (Scopus)


In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

Idioma originalInglés
Número de artículo224
Páginas (desde-hasta)1-27
Número de páginas27
PublicaciónJournal of Personalized Medicine
EstadoPublicada - nov. 2020
Publicado de forma externa


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