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

  • Zheqi Yu
  • Shufan Yang
  • Keliang Zhou
  • Amar Aggoun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)

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.

Keywords

Machine learning Endoscopy Cancer detection Texture analysis division 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zheqi Yu
    • 1
  • Shufan Yang
    • 2
  • Keliang Zhou
    • 2
  • Amar Aggoun
    • 1
  1. 1.Faculty of Science and EngineeringUniversity of WolverhamptonWolverhamptonUK
  2. 2.School of EngineeringUniversity of GlasgowGlasgowUK

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