A Low Computational Approach for Assistive Esophageal Adenocarcinoma and Colorectal Cancer Detection

Yu, Zheqi; Yang, Shufan; Zhou, Keliang; Aggoun, Amar

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.

Citation

Yu, Z., Yang, S., Zhou, K., & Aggoun, A. (2018, September). A Low Computational Approach for Assistive Esophageal Adenocarcinoma and Colorectal Cancer Detection. Presented at UK Workshop on Computational Intelligence, Nottingham

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: UK Workshop on Computational Intelligence
• Start Date: Sep 5, 2018
• End Date: Sep 7, 2018
• Online Publication Date: Aug 11, 2018
• Publication Date: 2019
• Deposit Date: Mar 11, 2021
• Publisher: Springer
• Pages: 169-178
• Book Title: Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence
• ISBN: 9783319979816
• DOI: https://doi.org/10.1007/978-3-319-97982-3_14
• Keywords: Machine learning, Endoscopy, Cancer detection, Texture analysis division
• Public URL: http://researchrepository.napier.ac.uk/Output/2752337