Colorectal Cancer Classification using Deep Convolutional Networks - An Experimental Study

Francesco Ponzio, Enrico Macii, Elisa Ficarra, Santa Di Cataldo

Abstract

The analysis of histological samples is of paramount importance for the early diagnosis of colorectal cancer (CRC). The traditional visual assessment is time-consuming and highly unreliable because of the subjectivity of the evaluation. On the other hand, automated analysis is extremely challenging due to the variability of the architectural and colouring characteristics of the histological images. In this work, we propose a deep learning technique based on Convolutional Neural Networks (CNNs) to differentiate adenocarcinomas from healthy tissues and benign lesions. Fully training the CNN on a large set of annotated CRC samples provides good classification accuracy (around 90% in our tests), but on the other hand has the drawback of a very computationally intensive training procedure. Hence, in our work we also investigate the use of transfer learning approaches, based on CNN models pre-trained on a completely different dataset (i.e. the ImageNet). In our results, transfer learning considerably outperforms the CNN fully trained on CRC samples, obtaining an accuracy of about 96% on the same test dataset.

Download


Paper Citation


in Harvard Style

Ponzio F., Macii E., Ficarra E. and Di Cataldo S. (2018). Colorectal Cancer Classification using Deep Convolutional Networks - An Experimental Study.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, ISBN 978-989-758-278-3, pages 58-66. DOI: 10.5220/0006643100580066


in Bibtex Style

@conference{bioimaging18,
author={Francesco Ponzio and Enrico Macii and Elisa Ficarra and Santa Di Cataldo},
title={Colorectal Cancer Classification using Deep Convolutional Networks - An Experimental Study},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,},
year={2018},
pages={58-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006643100580066},
isbn={978-989-758-278-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,
TI - Colorectal Cancer Classification using Deep Convolutional Networks - An Experimental Study
SN - 978-989-758-278-3
AU - Ponzio F.
AU - Macii E.
AU - Ficarra E.
AU - Di Cataldo S.
PY - 2018
SP - 58
EP - 66
DO - 10.5220/0006643100580066