An automated strabismus classification using machine learning algorithm for binocular vision management system

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Abstract

Binocular vision is a type of vision that allows an individual to perceive depth and distance using both eyes to create a single image of their environment. However, there is an illness called strabismus, where it is difficult for some people to focus on seeing things clearly at a time. There are many diagnoses that need to be done for doctors to diagnose whether patients suffer from strabismus or not. Besides, a new practitioner could lead to misdiagnosis due to lack of professional experience and knowledge. To overcome these limitations, a machine learning algorithm, which is a case-based reasoning, is developed to automate the strabismus classification. The results showed that the case-based reasoning algorithm provides 91.8% accuracy, 89.29% precision, 92.59% recall and 90.91% F1-Score. This shows that using the case-based reasoning algorithm can give better performance in classifying the class.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Accommodative amplitude; Case-based reasoning; Classification; Machine learning; Strabismus diagnosis
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	22 Mar 2024 06:25
Last Modified:	22 Mar 2024 06:25
URI:	http://umpir.ump.edu.my/id/eprint/40735
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