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Convolutional neural network-based diabetes diagnostic system via iridology technique

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Abstract

Iridology is a sort of complementary medicine using the patterns, colors, and other properties of the iris to gather systemic information about a person’s health status. To put it another way, iridology is the examination of the iris’ sensitive structures. Iridology is employed in conjunction with other approaches by physicians to better understand their patients’ needs. Examination and diagnosis, on the other hand, are highly subjective and dependent on medical experience of the physicians. It’s also a time-consuming and exhausting process for physicians. This study proposed a hybrid method of deep learning and image processing for a more objective examination and diabetes diagnosis based on iris images. The suggested method initially detected the iris boundary and then automatically extracted the pancreatic region in the iridology chart. Image processing steps allowed for the detection of the pancreatic region on the iris and its automatic segmentation from the eye image. Afterward, diabetes was diagnosed using convolutional neural networks on images, and the results were compared with different convolutional neural network architectures. It was concluded that the proposed method combined with VGG-16 architecture and automatic segmentation of the pancreatic region resulted in an accuracy of 80%, a sensitivity of 100%, a precision of 71.42%, a specificity of 60%, and an f1 score performance of 83.33%.

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References

  1. Adelina DC, Sigit R, Harsono T, Rochmad M (2017) Identification of diabetes in pancreatic organs using iridology. 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC). https://doi.org/10.1109/kcic.2017.8228573

  2. American Diabetes Association (2008) Diagnosis and classification of diabetes mellitus. Diabetes Care 32(Supplement1):S62–S67. https://doi.org/10.2337/dc09-s062

    Article  Google Scholar 

  3. Aminah R, Saputro AH (2019) Diabetes prediction system based on iridology using machine learning. 2019 6th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE). https://doi.org/10.1109/icitacee.2019.8904125

  4. Aminah R, Saputro AH (2019) Application of machine learning techniques for diagnosis of diabetes based on iridology. 2019 International Conference on Advanced Computer Science and Information Systems (ICACSIS). https://doi.org/10.1109/ICACSIS47736.2019.8979755

  5. Andana SN, Novamizanti L, Ramatryana NA (2019) Measurement of cholesterol conditions of eye image using Fuzzy Local Binary Pattern (FLBP) and Linear Regression. 2019 IEEE International Conference on Signals and Systems (ICSigSys)

  6. Beagley J, Guariguata L, Wei C, Motala AA (2013) Global estimates of undiagnosed diabetes in adults. Diabetes Res Clin Pract 103(2):150–160. https://doi.org/10.1016/j.diabres.2013.11.001

    Article  Google Scholar 

  7. Behera SK, Sethy PK (2021) Categorization of Common Pigmented Skin Lesions (CPSL) using multi-deep features and support vector Machine. https://doi.org/10.21203/rs.3.rs-136988/v1

  8. Chang S, Chen X, Duan J, Mou X (2020) A CNN based hybrid ring artifact reduction algorithm for CT images. In: IEEE Transactions on Radiation and Plasma Medical Sciences. https://doi.org/10.1109/TRPMS.2020.2983391

  9. Daugman J (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161. https://doi.org/10.1109/34.244676

    Article  Google Scholar 

  10. Daugman J (2004) How iris recognition works. IEEE Trans Circuits Syst Video Technol 14(1):21–30

  11. Deeba K, Amutha B (2020) ResNet - Deep Neural Network architecture for leaf disease classification. Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2020.103364

    Article  Google Scholar 

  12. Dewi AK, Novianty A, Purboyo TW (2016) Stomach dısorder detectıon through the irıs image usıng backpropagatıon neural network. 2016 International Conference on Informatics and Computing (ICIC)

  13. Ernst E, Societas (2008) Healing, hype or harm? A critical analysis of complementary or alternative medicine. (Societas). Imprint Academic

  14. Goodfellow I, Bengio Y, Courville A (2015) Deep learning (Adaptive computation and machine learning). The MIT Press, Cambridge, pp xxii, 775 pages

  15. Guraksin GE, Uğuz H, Baykan OK (2016) Bone age determination in young children (newborn to 6 years old) using support vector machines. Turk J Electr Eng Comput Sci 24:1693–1708. https://doi.org/10.3906/elk-1305-271

    Article  Google Scholar 

  16. Güraksın GE, Barın S, Özgül E, Kaya F (2021)COVID-19 diagnosis using deep learning. Düzce Univ J Sci Technol 9(2021):8–23. https://doi.org/10.29130/dubited.866124

  17. Hussein SE, Hassan OA, Granat MH (2013) Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks. Biomed Signal Process Control 8(6):534–541. https://doi.org/10.1016/j.bspc.2013.04.006

    Article  Google Scholar 

  18. Itoh H, Lu Z, Mori Y, Misawa M, Oda M, Kudo S, Mori K (2020) Visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endoscytoscopic images based on CNN weights analysis. Proc. SPIE 11314, Medical Imaging Computer-Aided Diagnosis, 1131438. https://doi.org/10.1117/12.2549532

  19. Jensen B (1982) Iridology: The science and practice in the healing arts.  California: Bernard Jensen. Vol. 2

  20. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105. https://doi.org/10.1145/3065386

  21. Kusuma FD, Kusumaningtyas EM, Barakbah AR, Hermawan AA (2018) Heart abnormalities detection through iris based on mobile. 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)

  22. Lecun Y, Bottou L, Bengio Y, Haffner P (1998)Gradient-based learning applied to document recognition. Proc IEEE, 86(11):2278–2324. https://doi.org/10.1109/5.726791

  23. Lodin A, Demea S (2009) Design of an iris-based medical diagnosis system. 2009 International Symposium on Signals, Circuits and Systems. https://doi.org/10.1109/isscs.2009.5206187

  24. Lodin A, Kovacs L, Demea S (2007) Interface of an Iris Detection Program. 2007 30th International Spring Seminar on Electronics Technology (ISSE). https://doi.org/10.1109/isse.2007.4432918

  25. Ma L, Li N (2007) Texture feature extraction and classification for iris diagnosis. Med Biometrics 168–175. https://doi.org/10.1007/978-3-540-77413-6_22

  26. Ma L, Wang K, Zhang D (2009) A universal texture segmentation and representation scheme based on ant colony optimization for iris image processing. Comput Math Appl 57(11–12):1862–1868. https://doi.org/10.1016/j.camwa.2008.10.012

    Article  MATH  Google Scholar 

  27. Permatasari LI, Novianty A, Purboyo TW (2016) Heart disorder detection based on computerized iridology using support vector machine. The 2016 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC)

  28. Putri R, Saputro AH (2019) Implementation of neural network classification for diabetes mellitus prediction system through iridology image. 2019 6th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE). https://doi.org/10.1109/icitacee.2019.8904182

  29. Samikannu R (2020) An efficient image analysis framework for the classification of glioma brain images using CNN approach. Comput Mater Continua 63(3):1133–1142. https://doi.org/10.32604/cmc.2020.08578

    Article  Google Scholar 

  30. Sarika GS, Madhuri SJ (2016) Automated detection of cholesterol presence using iris recognition algorithm. Int J Comput Appl 133(6):41–45

    Google Scholar 

  31. Sethy PK, Behera SK, Anitha K, Pandey C, Khan MR (2021) Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison. J X-Ray Sci Technol 1 Jan 2021:1–14. https://doi.org/10.3233/XST-200784

    Article  Google Scholar 

  32. Shen B, Xu Y, Lu G, Zhang D (2007) Detecting iris lacunae based on Gaussian filter. Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007). https://doi.org/10.1109/iihmsp.2007.4457533

  33. Simon A, Worthen DM, Mitas JA (1979) An evaluation of iridology. J Am Med Assoc 242(13):1385. https://doi.org/10.1001/jama.1979.03300130029014

    Article  Google Scholar 

  34. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. 3rd Int. Conf. Learn. Represent. ICLR 2015. ArXiv:1409.1556

  35. Szegedy C et al (2015) Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594.7

  36. Velia D, Saputro AH (2020) Designing diabetes mellitus detection system based on iridology with convolutional neural network modeling. 2020 4th International Conference on Informatics and Computational Sciences (ICICoS). https://doi.org/10.1109/icicos51170.2020.9299081

  37. Wani MA, Bhat FA, Afzal S, Khan AI (2020) Advances in deep learning. Studies in Big Data. https://doi.org/10.1007/978-981-13-6794-6

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Acknowledgements

This study was supported by the Unit of Scientific Research and Projects of Afyon Kocatepe University (Project No: 18.FEN.BIL.28).

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Authors and Affiliations

  1. Department of Biomedical Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey

    Merve Nur Önal

  2. Department of Computer Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey

    Gür Emre Güraksin

  3. Department of Ophthalmology, Bursa City Hospital, Bursa, Turkey

    Reşat Duman

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  1. Merve Nur Önal
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  2. Gür Emre Güraksin
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  3. Reşat Duman
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Correspondence to Gür Emre Güraksin.

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Önal, M.N., Güraksin, G.E. & Duman, R. Convolutional neural network-based diabetes diagnostic system via iridology technique. Multimed Tools Appl 82, 173–194 (2023). https://doi.org/10.1007/s11042-022-13291-3

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  • DOI: https://doi.org/10.1007/s11042-022-13291-3

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