Novel Keratoconus Detection Method Using Smartphone

Behnam Askarian, Fatemehsadat Tabei, Grace Anne Tipton, Jo Woon Chong

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

1 Scopus citations

Abstract

Keratoconus is a progressive corneal disease which may cause blindness if it is not detected in the early stage. In this paper, we propose a portable, low-cost, and robust keratoconus detection method which is based on smartphone camera images. A gadget has been designed and manufactured using 3-D printing to supplement keratoconus detection. A smartphone camera with the gadget provides more accurate and robust keratoconus detection performance. We adopted the Prewitt operator for edge detection and the support vector machine (SVM) to classify keratoconus eyes from healthy eyes. Experimental results show that the proposed method can detect mild, moderate, advanced, and severe stages of keratoconus with 89% accuracy on average.

Original languageEnglish
Title of host publication2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages60-62
Number of pages3
ISBN (Electronic)9781728138121
DOIs
StatePublished - Nov 2019
Event2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019 - Bethesda, United States
Duration: Nov 20 2019Nov 22 2019

Publication series

Name2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019

Conference

Conference2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019
CountryUnited States
CityBethesda
Period11/20/1911/22/19

Keywords

  • Cornea
  • Corneal Topography
  • Keratoconus
  • Smartphone
  • Support Vector Machine

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