Early detection of oesophageal cancer through colour contrast enhancement for data augmentation

Gao, Xiaohong W. ORCID: https://orcid.org/0000-0002-8103-6624, Taylor, Stephen, Pang, Wei, Lu, Xin and Braden, Barbara (2022) Early detection of oesophageal cancer through colour contrast enhancement for data augmentation. Medical Imaging 2022: Computer-Aided Diagnosis - Proceedings of SPIE. In: SPIE Medical Imaging: Computer-Aided Diagnosis, 21-24 Feb 2022, San Diego, USA. . ISSN 0277-786X [Conference or Workshop Item] (Accepted/In press)

[img]
Preview
PDF - Final accepted version (with author's formatting)
Download (642kB) | Preview

Abstract

While white light imaging (WLI) of endoscopy has been set as the gold standard for screening and detecting oesophageal squamous cell cancer (SCC), the early signs of SCC are often missed (1 in 4) due to its subtle change of early onset of SCC. This study firstly enhances colour contrast of each of over 600 WLI images and their accompanying narrow band images (NBI) applying CIE colour appearance model CIECAM02. Then these augmented data together with the original images are employed to train a deep learning based system for classification of low grade dysplasia (LGD), SCC and high grade dysplasia (HGD). As a result, the averaged colour difference (ΔE) measured using CIEL*a*b* increased from 11.60 to 14.46 for WLI and from 17.52 to 32.53 for NBI in appearance between suspected regions and their normal neighbours. When training a deep learning system with added enhanced contrasted WLI images, the sensitivity, specific and accuracy for LGD increases by 10.87%, 4.95% and 6.76% respectively. When training with enhanced both WLI and NBI images, these measures for LGD increases by 14.83%, 4.89% and 7.97% respectively, the biggest increase among three classes of SCC, HGD and LGD. In average, the sensitivity, specificity and accuracy for these three classes are 88.26%, 94.44% and 92.63% respectively for classification of SCC, HGD and LGD, being comparable or exceeding existing published work.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 34582
Notes on copyright: Copyright 2022 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.
Useful Links:
Depositing User: Xiaohong Gao
Date Deposited: 21 Jan 2022 17:19
Last Modified: 22 Feb 2022 04:04
URI: https://eprints.mdx.ac.uk/id/eprint/34582

Actions (login required)

View Item View Item

Statistics

Downloads
Activity Overview
5Downloads
42Hits

Additional statistics are available via IRStats2.