Support Vector Machine for Keratoconus Detection by Using Topographic Maps with the Help of Image Processing Techniques
Journal Title: IOSR Journal of Pharmacy and Biological Sciences (IOSR-JPBS) - Year 2017, Vol 12, Issue 6
Abstract
In recent years many researchers tried to find an accurate method to diagnose eye disease, researchers used different methods and devices for that. In this paper a method is present which depends on the details extracted from the topographic maps to detect Keratoconus (KC) that affects the cornea in diseased eye; with the help of image processing techniques. Twelve features (12) have been extracted from topographic maps collected from the Pentacam which is a device that acquired maps of the cornea and provide an information of the health of the eye, and applying these features to the support vector machine SVM (which is a supervised classification) to detect whether the cornea is healthy or diseased. Results showed that there is accuracy of about 90% for the tested data. Each normal indication will be written as Ok; and a Red flag will be written for the abnormal indications.
Authors and Affiliations
Alyaa H. Ali, Nebras H. Ghaeb, Zahraa M. Musa
Keywords
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- EP ID EP392579
- DOI 10.9790/3008-1206065058
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