Exploring Deep Learning and Transfer Learning for Colonic Polyp ClassificationReport as inadecuate




Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification - Download this document for free, or read online. Document in PDF available to download.

Computational and Mathematical Methods in Medicine - Volume 2016 2016, Article ID 6584725, 16 pages -

Research Article

Department of Computer Sciences, University of Salzburg, Salzburg, Austria

Department of Computer Sciences, Federal University of Tocantins, Palmas, TO, Brazil

St. Elisabeth Hospital, Vienna, Austria

Received 10 August 2016; Accepted 4 October 2016

Academic Editor: Ayman El-Baz

Copyright © 2016 Eduardo Ribeiro et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.





Author: Eduardo Ribeiro, Andreas Uhl, Georg Wimmer, and Michael Häfner

Source: https://www.hindawi.com/



DOWNLOAD PDF




Related documents