Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification.
Comput Math Methods Med
; 2016: 6584725, 2016.
Article
em En
| MEDLINE
| ID: mdl-27847543
ABSTRACT
Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the "off-the-shelf" CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and "off-the-shelf" CNNs features can be a good approach to further improve the results.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Pólipos do Colo
/
Diagnóstico por Computador
/
Colonoscopia
/
Endoscopia
/
Aprendizado de Máquina
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Comput Math Methods Med
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
Brasil