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

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


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.


Machine learning Endoscopy Cancer detection Texture analysis division 


  1. 1.
    Sun, L., Subar, A.F., Bosire, C., Dawsey, S.M., Kahle, L.L., Zimmerman, T.P., Abnet, C.C., Heller, R., Graubard, B.I., Cook, M.B.: Dietary flavonoid intake reduces the risk of head and neck but not esophageal or gastric cancer in us men and women. J. Nutr. 147(9), 1729–1738 (2017)Google Scholar
  2. 2.
    Velusamy, P.D., Karandharaj, P.: Medical image processing schemes for cancer detection: a survey. In: Proceedings of 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), pp. 1–6. IEEE (2014)Google Scholar
  3. 3.
    Watson, T.F., Neil, M.A.A., Juškaitis, R., Cook, R.J., Wilson, T.: Video-rate confocal endoscopy. J. Microsc. 207(1), 37–42 (2002)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Qi, J., Le, M., Li, C., Zhou, P.: Global and local information based deep network for skin lesion segmentation. arXiv preprint arXiv: 1703.05467 (2017)
  5. 5.
    Yu, Z., Jiang, X., Wang, T., Lei, B.: Aggregating deep convolutional features for melanoma recognition in dermoscopy images. In: International Workshop on Machine Learning in Medical Imaging, pp. 238–246. Springer, Cham (2017)Google Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  7. 7.
    Bhushan, N., Ravishankar Rao, A., Lohse, G.L.: The texture lexicon: understanding the categorization of visual texture terms and their relationship to texture images. Cogn. Sci. 21(2), 219–246 (1997)Google Scholar
  8. 8.
    Hawkes, P.W.: Advances in Imaging and Electron Physics, vol. 116. Academic Press, Cambridge (2001)Google Scholar
  9. 9.
    Amadasun, M., King, R.: Textural features corresponding to textural properties. IEEE Trans. Syst. Man Cybern. 19(5), 1264–1274 (1989)CrossRefGoogle Scholar
  10. 10.
    Ye, M., Giannarou, S., Meining, A., Yang, G.-Z.: Online tracking and retargeting with applications to optical biopsy in gastrointestinal endoscopic examinations. Med. Image Anal. 30, 144–157 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

Personalised recommendations