224
Views
12
CrossRef citations to date
0
Altmetric
Research Article

Comparative analysis of classification based algorithms for diabetes diagnosis using iris images

ORCID Icon &
Pages 35-42 | Received 16 Aug 2017, Accepted 28 Nov 2017, Published online: 04 Jan 2018

References

  • Fonseca VA. Defining and characterizing the progression of type 2 diabetes. Diabetes Care. 2009;32:S151–S156.
  • Beagley J, Guariguata L, Weil C, et al. Global estimates of undiagnosed diabetes in adults. Diabetes Res Clin Pract. 2013;103:150–160.
  • Middle T, Journal E. Effectiveness of complementary and alternative medicine – call for a “black box” research agenda. Middle Eur J Med. 2005;7:239–240.
  • Harris PE, Cooper KL, Relton C, et al. Prevalence of complementary and alternative medicine (CAM) use by the general population: a systematic review and update. Int J Clin Pract. 2012;66:924–939.
  • Jensen. Iridology simplified. 5th ed. Escondido (CA): Iridologists International; 2011.
  • Knipschild P. Looking for gall bladder disease in the patient's iris. BMJ. 1988;297:1578–1581.
  • Ma L, Wang K, Zhang D. A universal texture segmentation and representation scheme based on ant colony optimization for iris image processing. Comput Math Appl. 2009;57:1862–1868.
  • Hussein SE, Hassan OA, Granat MH. Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks. Biomed Signal Process Control. 2013;8:534–541.
  • Ramlee RA, Aziz KA, Esro M, et al. Automated detecting arcus senilis, symptom for cholesterol presence using iris recognition algorithm. J Telecommun Electron Comput Eng. 2011;3:29–39.
  • Ramlee RA, Azha K, Singh R, et al. Detecting cholesterol presence with iris recognition algorithm. In: Riaz Z, editor. Biometric System, Design and Applications. Croatia: InTech Open; 2010. p. 129–148.
  • Bansal A, Agarwal R, Sharma RK. Determining diabetes using iris recognition system. Int J Diabetes Dev Countries. 2015;35:432–438.
  • Passarella R, Fachrurrozi M. Development of iridology system database for colon disorders identification using Image processing. Indian J Bioinform Biotechnol. 2013;2:100–103.
  • Parodi MB, Bondel E, Saviano S. Iris indocyanine green angiography in pseudoexfoliation syndrome and capsular glaucoma. Acta Ophthalmol Scand. 2000;77:437–442.
  • Azilah S, Paper F. Identification of vagina and pelvis from iris region using artificial neural network. Teknologi. 2015;76:91–95.
  • Ma L, Zhang D, Li N, et al. Iris-based medical analysis by geometric deformation features. IEEE J Biomed Heal Informatics. 2013;17:223–231.
  • LodinVasile A, Iozan ML. Iris-based remote medical diagnosis system. Carpathian J Electron Comput Eng. 2009;2:26–29.
  • Bhatia PS, Atole K, Kamble P, et al. Methodology for detecting diabetic presence from iris image analysis. Int J Adv Res Comput Eng Technol. 2015;4:776–779.
  • Othman Z, Satria Prabuwono A. Preliminary study on iris recognition system: tissues of body organs in iridology. IEEE EMBS Conference on Biomedical Engineering and Sciences (ICEBES), Kuala Lumpur. IEEE Xplore; 2010. p. 115–119.
  • Um J, An N, Yang G, et al. Novel approach of molecular genetic understanding of iridology: relationship between iris constitution and angiotensin converting enzyme gene polymorphism. Am J Chin Med. 2005;33:501–505.
  • Liam L, Chekima WA, Fan LC, et al. Tumor detection using iris pattern. In: 4th Annual Seminar of National Science Fellowship; 2004. p. 491–493.
  • Helwan A. ITDS: iris tumor detection system using image processing techniques. Int J Sci Eng Res. 2014;5:76–80.
  • Dody IP, Ketut LI, Purnama E, et al. Abnormal condition detection of pancreatic beta-cells as the cause of diabetes mellitus based on iris image. IEEE International Conference on Instrumentation, Communication, Information Technology and Biomedical Engineering; 2011. p. 150–155.
  • Banzi JF, Xue Z. An automated tool for non-contact, real time early detection of diabetes by computer vision. Int J Mach Learn Comput. 2015;5:1–5.
  • Hareva D, Lukas S, Suharta N. The smart device for healthcare service: iris diagnosis application. In: 11th International Conference on ICT and Knowledge Engineering; 2013. p. 1–6.
  • Parmar C, Grossmann P, Bussink J, et al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep. 2015;5:1–11.
  • Daugman J. The importance of being random: statistical principles of iris recognition. Pattern Recogn. 2003;36:279–291.
  • Daugman J, Downing C. Epigenetic randomness, complexity and singularity of human iris patterns. Proc Biol Sci. 2001;268:1737–1740.
  • Daugman J. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Machine Intell. 1993;15: 1148–1161.
  • Daugman J. How iris recognition works. IEEE Trans Circuits Syst Video Technol. 2004;14:715–739.
  • Daugman J. Iris recognition. In: Tutorial, on International Conference on Biometrics; 2012. p. 1–118.
  • Wildes RP. Iris recognition: an emerging biometric technology. Proc IEEE. 1997;85:1348–1363.
  • Sruthi TK, Jini KM. A literature review on iris segmentation techniques for iris recognition systems. IOSR-JCE. 2013;11:46–50.
  • Abdul Jalil N, Sahak R, Saparon A. Iris localization using colour segmentation and circular hough transform. EEE-EMBS Conference on Biomedical Engineering and Sciences, Langkawi, Malaysia; 2012. p. 784–788.
  • Vatsa M, Member S, Singh R, et al. Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans Syst Man Cybern B. 2008;38:1021–1035.
  • Sankowski W, Grabowski K, Napieralska M, et al. Reliable algorithm for iris segmentation in eye image. Image Vis. Comput. 2010;28:231–237.
  • Alonso-Fernandez F, Bigun J. Preprint irisSeg: a fast and robust iris segmentation framework for non-ideal iris images. In: 9th IAPR International Conference on Biometrics; 2016. p. 1–9.
  • He Z, Tan T, Sun Z, et al. Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans Pattern Anal Mach Intell. 2008;31:1670–1684.
  • Jensen B. Iridology Chart (n.d.). [cited 2016 Nov 1] Available from: http://www.bernardjensen.com/
  • De Marsico M, Petrosino A, Ricciardi S. Iris recognition through machine learning techniques: a survey. Pattern Recogn Lett. 2016;82:106–115.
  • FernCernadas ME. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15:3133–3181.
  • Wibawa AD, Purnomo MH, Early detection on the condition of Pancreas organ as the cause of diabetes mellitus by real time iris image processing. In: IEEE Asia-Pacific Conference on Circuits Systems, Proceedings, APCCAS ; 2006. p. 1008–1010.
  • Sivasankar K. FCM based iris image analysis for tissue imbalance stage identification. In: International Conference on Emerging Trends in Science, Engineering and Technology; 2012. p. 210–215.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.