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Detecting keratoconus by using SVM and decision tree classifiers with the aid of image processing
Other Title(s)
تشخيص مرض القرنية المخروطية باستخدام طريقتا التصنيف و طرق معالجة الصور Decision Tree و SVM
Joint Authors
Musa, Zahra Malik
Ali, Alya Husayn
Ghaib, Nibras Husayn
Source
Issue
Vol. 16, Issue 4 (sup) (31 Dec. 2019), pp.1022-1029, 8 p.
Publisher
University of Baghdad College of Science for Women
Publication Date
2019-12-31
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Abstract EN
Researchers used different methods such as image processing and machine learning techniques in addition to medical instruments such as Placido disc, Keratoscopy, Pentacam;to help diagnosing variety of diseases that affect the eye.
Our paper aims to detect one of these diseases that affect the cornea, which is Keratoconus.
This is done by using image processing techniques and pattern classification methods.
Pentacam is the device that is used to detect the cornea’s health; it provides four maps that can distinguish the changes on the surface of the cornea which can be used for Keratoconus detection.
In this study, sixteen features were extracted from the four refractive maps along with five readings from the Pentacam software.
The classifiers utilized in our study are Support Vector Machine (SVM) and Decision Trees classification accuracy was achieved 90% and 87.5%, respectively of detecting Keratoconus corneas.
The features were extracted by using the Matlab (R2011 and R 2017) and Orange canvas (Pythonw).
American Psychological Association (APA)
Musa, Zahra Malik& Ghaib, Nibras Husayn& Ali, Alya Husayn. 2019. Detecting keratoconus by using SVM and decision tree classifiers with the aid of image processing. Baghdad Science Journal،Vol. 16, no. 4 (sup), pp.1022-1029.
https://search.emarefa.net/detail/BIM-935386
Modern Language Association (MLA)
Ghaib, Nibras Husayn…[et al.]. Detecting keratoconus by using SVM and decision tree classifiers with the aid of image processing. Baghdad Science Journal Vol. 16, no. 4 (Supplement) (2019), pp.1022-1029.
https://search.emarefa.net/detail/BIM-935386
American Medical Association (AMA)
Musa, Zahra Malik& Ghaib, Nibras Husayn& Ali, Alya Husayn. Detecting keratoconus by using SVM and decision tree classifiers with the aid of image processing. Baghdad Science Journal. 2019. Vol. 16, no. 4 (sup), pp.1022-1029.
https://search.emarefa.net/detail/BIM-935386
Data Type
Journal Articles
Language
English
Notes
Text in English ; abstracts in English and Arabic.
Record ID
BIM-935386