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IJMLC 2020 Vol.10(2): 233-239 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.925

Onyxray: A Mobile-Based Nail Diseases Detection Using Custom Vision Machine Learning

Sholomon L. Pinoliad, Duanne Austin N. Dichoso, Arlene R. Caballero, and Erlito M. Albina

Abstract—Nails serves as a great way to discover several underlying systemic diseases of a person. There are different nail abnormalities that are associated with systemic diseases. The objective of the study is to utilize mobile camera that will allow users to capture of nail. Through this, the application will suggest several systemic diseases found based on the uploaded nail image by the user. The methods applied in this study includes the use of Custom Vision API that process images to analyze certain nail abnormalities. This study concludes that systemic diseases can be identify through nail abnormalities, so that the proponents developed an application that will analyze a fingernail image and will provide systemic diseases suggestions.

Index Terms—Custom vision, mobile application, nail abnormalities, systemic diseases.

The authors are with the College of Technology, Lyceum of the Philippines University, Manila, Philippines (e-mail: arlene.caballero@lpu.edu.ph, erlito.albina@lpu.edu.ph).

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Cite: Sholomon L. Pinoliad, Duanne Austin N. Dichoso, Arlene R. Caballero, and Erlito M. Albina, "Onyxray: A Mobile-Based Nail Diseases Detection Using Custom Vision Machine Learning," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 233-239, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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