Polyp Classification and Clustering from Endoscopic Images using Competitive and Convolutional Neural Networks

Avish Kabra, Yuji Iwahori, Hiroyasu Usami, M. Bhuyan, Naotaka Ogasawara, Kunio Kasugai

2019

Abstract

Understanding the type of Polyp present in the body plays an important role in medical diagnosis. This paper proposes an approach to classify and cluster the polyp present in an Endoscopic scene into malignant or benign class. CNN and Self Organizing Maps are used to classify and cluster from white light and Narrow Band (NBI) Endoscopic Images . Using Competitive Neural Network different polyps available from previous data are plotted with the new polyp according to their structural similarity. Such kind of presentation not only help the doctor in it’s easy understanding but also helps him to know what kind of medical procedures were followed in similar cases.

Paper Citation

in Harvard Style

Kabra A., Iwahori Y., Usami H., Bhuyan M., Ogasawara N. and Kasugai K. (2019). Polyp Classification and Clustering from Endoscopic Images using Competitive and Convolutional Neural Networks.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 446-452. DOI: 10.5220/0007353204460452

in Bibtex Style

@conference{icpram19,
author={Avish Kabra and Yuji Iwahori and Hiroyasu Usami and M. Bhuyan and Naotaka Ogasawara and Kunio Kasugai},
title={Polyp Classification and Clustering from Endoscopic Images using Competitive and Convolutional Neural Networks},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={446-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007353204460452},
isbn={978-989-758-351-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Polyp Classification and Clustering from Endoscopic Images using Competitive and Convolutional Neural Networks
SN - 978-989-758-351-3
AU - Kabra A.
AU - Iwahori Y.
AU - Usami H.
AU - Bhuyan M.
AU - Ogasawara N.
AU - Kasugai K.
PY - 2019
SP - 446
EP - 452
DO - 10.5220/0007353204460452