An affordable and easy-to-use diagnostic method for keratoconus detection using a smartphone

Behnam Askarian, Fatemehsadat Tabei, Amin Askarian, Jo Woon Chong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations


Recently, smartphones are used for disease diagnosis and healthcare. In this paper, we propose a novel affordable diagnostic method of detecting keratoconus using a smartphone. Keratoconus is usually detected in clinics with ophthalmic devices, which are large, expensive and not portable, and need to be operated by trained technicians. However, our proposed smartphone-based eye disease detection method is small, affordable, portable, and it can be operated by patients in a convenient way. The results show that the proposed keratoconus detection method detects severe, advanced, and moderate keratoconus with accuracies of 93%, 86%, 67%, respectively. Due to its convenience with these accuracies, the proposed keratoconus detection method is expected to be applied in detecting keratoconus at an earlier stage in an affordable way.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Nicholas Petrick
ISBN (Electronic)9781510616394
StatePublished - 2018
EventMedical Imaging 2018: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 12 2018Feb 15 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2018: Computer-Aided Diagnosis
Country/TerritoryUnited States


  • Cornea
  • Corneal topography
  • Image processing
  • Keratoconus
  • Smartphone


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