@inproceedings{da547e3b515c46caa5bdb4f0cc114399,
title = "Novel Keratoconus Detection Method Using Smartphone",
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.",
keywords = "Cornea, Corneal Topography, Keratoconus, Smartphone, Support Vector Machine",
author = "Behnam Askarian and Fatemehsadat Tabei and Tipton, {Grace Anne} and Chong, {Jo Woon}",
year = "2019",
month = nov,
doi = "10.1109/HI-POCT45284.2019.8962648",
language = "English",
series = "2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "60--62",
booktitle = "2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019",
note = "2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019 ; Conference date: 20-11-2019 Through 22-11-2019",
}