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

Muhammad Amirul Isyraf, Rohismadi and Anis Farihan, Mat Raffei and Nor Saradatul Akmar, Zulkifli and Mohd. Hafidz, Ithnin and Shah Farez, Othman (2023) An automated strabismus classification using machine learning algorithm for binocular vision management system. In: 8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25 - 27 August 2023 , Penang, Malaysia. 487 -492.. ISBN 979-835031093-1

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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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