Computer Aided Diagnosis of Gastrointestinal Diseases Based on Iridology

  • Enrique V. CarreraEmail author
  • Jennifer Maya
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 895)


Gastrointestinal diseases are important causes of mortality and expenses around the world. Since conventional methods for diagnosing gastrointestinal problems are expensive and invasive, alternative medicine techniques emerge as a possibility for helping physicians in this type of diagnosis. Hence, this work proposes a computer aided diagnosis system based on iridology for early detection of gastrointestinal diseases. The proposed system employs image processing and machine learning algorithms to identify gastrointestinal disorders in iris images. The evaluation of the system uses 100 iris images showing a maximum accuracy of 96% and a predictive capacity of 99%. This work shows that alternative medicine techniques have potential for diagnosing problems associated to gastrointestinal disorders.


Gastrointestinal diseases Iridology techniques Image processing Machine learning 



Authors would like to thank Dr. Telmo De la Torre for helping us to diagnose gastrointestinal diseases in the iris images used in this work. This work was partially supported by the Universidad de las Fuerzas Armadas ESPE under Research Grant 2015-PIC-004.


  1. 1.
    Daugman, J.: How iris recognition works. In: The Essential Guide to Image Processing, pp. 715–739. Elsevier (2009)Google Scholar
  2. 2.
    Gonzalez, R.C., Eddins, S.L.: Digital Image Processing Using Matlab (2017)Google Scholar
  3. 3.
    Haroon, D.: Python Machine Learning Case Studies: Five Case Studies for the Data Scientist, vol. 1. Apress, New York City (2017)Google Scholar
  4. 4.
    National Institutes of Health and US Department of Health and Human Services: Opportunities and challenges in digestive diseases research: recommendations of the national commission on digestive diseases. National Institutes of Health, Bethesda, MD (2009)Google Scholar
  5. 5.
    Iriso: Iriso camera, July 2015.
  6. 6.
    Jensen, B.: Iridology Simplified. Book Publishing Company, Summertown (2012)Google Scholar
  7. 7.
    Laganière, R.: OpenCV Computer Vision Application Programming Cookbook, vol. 2. Packt Publishing Ltd, Birmingham (2014)Google Scholar
  8. 8.
    Mangalam, J.S.S., Deepa, S.: Analysis of iridology using Zhang-Suen’s algorithm. Int. J. Adv. Res. Comput. Sci. 8(3), 1233–1237 (2017). Scholar
  9. 9.
    Samant, P., Agarwal, R.: Diagnosis of diabetes using computer methods: soft computing methods for diabetes detection using iris. Power 651, 63915 (2017)Google Scholar
  10. 10.
    Sivasankar, K., Sujaritha, M., Pasupathi, P., Muthukumar, S.: FCM based iris image analysis for tissue imbalance stage identification. In: Emerging Trends in Science, Engineering and Technology, pp. 210–215. IEEE (2012)Google Scholar
  11. 11.
    Theodoridis, S., Pikrakis, A., Koutroumbas, K., Cavouras, D.: Introduction to Pattern Recognition: A Matlab Approach. Academic Press, Cambridge (2010)Google Scholar
  12. 12.
    Van der Walt, S., et al.: Scikit-image: image processing in Python. PeerJ 2, e453 (2014)Google Scholar
  13. 13.
    Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)Google Scholar
  14. 14.
    Wilson, A.D.: Recent applications of electronic-nose technologies for the noninvasive early diagnosis of gastrointestinal diseases. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 2, p. 147 (2017)Google Scholar
  15. 15.
    Wong, T.T.: Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit. 48(9), 2839–2846 (2015)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Departamento de Eléctrica y ElectrónicaUniversidad de las Fuerzas Armadas ESPESangolquíEcuador

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