A low computational approach for assistive esophageal adenocarcinoma and colorectal cancer detection

Yu, Z., Yang, S. , Zhou, K. and Aggoun, A. (2018) A low computational approach for assistive esophageal adenocarcinoma and colorectal cancer detection. Advances in Intelligent Systems and Computing, 840, pp. 169-178. (doi:10.1007/978-3-319-97982-3_14)

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Abstract

In this paper, we aim to develop a low-computational system for real-time image processing and analysis in endoscopy images for the early detection of the human esophageal adenocarcinoma and colorectal cancer. Rich statistical features are used to train an improved machine-learning algorithm. Our algorithm can achieve a real-time classification of malign and benign cancer tumours with a significantly improved detection precision compared to the classical HOG method as a reference when it is implemented on real time embedded system NVIDIA TX2 platform. Our approach can help to avoid unnecessary biopsies for patients and reduce the over diagnosis of clinically insignificant cancers in the future.

Item Type:Articles
Additional Information:Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840) ISBN 9783319979816
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Yang, Dr Shufan and Zhou, Dr Keliang
Authors: Yu, Z., Yang, S., Zhou, K., and Aggoun, A.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Advances in Intelligent Systems and Computing
Publisher:Springer
ISSN:2194-5357
ISSN (Online):2194-5365
Copyright Holders:Copyright © 2018 Springer Nature Switzerland AG
First Published:First published in Advances in Intelligent Systems and Computing 840:169-178
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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