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

• Main Authors: Rohismadi, Muhammad Amirul Isyraf, Mat Raffei, Anis Farihan, Zulkifli, Nor Saradatul Akmar, Ithnin, Mohd. Hafidz, Othman, Shah Farez
• Format: Proceeding Paper
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers (IEEE) 2023
• Subjects: T Technology (General)
• Online Access: http://irep.iium.edu.my/109266/8/109266_An%20automated%20strabismus%20classification%20using%20machine%20learning%20algorithm_Scopus.pdf; http://irep.iium.edu.my/109266/15/109266_An%20automated%20strabismus%20classification%20using%20machine%20learning%20algorithm.pdf; http://irep.iium.edu.my/109266/; https://ieeexplore.ieee.org/document/10256291; https://doi.org/10.1109/ICSECS58457.2023.10256291
• Institution: Universiti Islam Antarabangsa Malaysia

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.