An Automated Strabismus Classification Using Machine Learning Algorithm for Binocular Vision Management System - IEEE conference paper abstract summary

Aug 5, 2026

Binocular vision supports depth perception by combining both eyes into a single visual image. Strabismus disrupts clear focusing and often requires expert diagnosis, and inexperienced practitioners may contribute to misclassification due to limited clinical experience. An automated approach based on a case-based reasoning machine learning algorithm is developed for strabismus classification. Reported performance reaches 91.8% accuracy, 89.29% precision, 92.59% recall, and 90.91% F1-score, supporting improved diagnostic class classification.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages3

Reading Time8 min

AccessFree to download

Abstract

Problem: strabismus diagnosis and misclassification risk

Method: case-based reasoning machine learning

Results: accuracy, precision, recall, F1-score

12/26/23 , 2:56 PM Scopus-Print Document Documents Isyraf Rohismadi, M.A.a , Mat Raffei, A. F.a , Akmar Zulkifli, N.S.a , Ithnin, M. H. b , Othman, S. F.b An Automated Strabismus Classification Using Machine Learning Algorithm for Binocular Vision Management System (2023) 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , pp. 487-492. DOI: 10.1109/ICSECS58457 .2023.10256291 a Universiti Malaysia Pahang Al-Sultan Abdullah, Faculty of Computing, Pekan, Pahang, 26600, Malaysia b Kulliyah of Medicine International Islamic University Malaysia, Kuantan, Pahang, 25200, Malaysia 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. © 2023 IEEE. Author Keywords Accommodative amplitude; case-based reasoning; classification; machine learning; strabismus diagnosis Index Keywords Binocular vision, Computer aided diagnosis, Learning algorithms, Machine learning, Stereo image processing; Accommodative amplitude, Casebased reasonings (CBR), Machine learning algorithms, Machine-learning, Management systems, Professional experiences, Reasoning algorithms, Single images, Strabismus diagnose, Types of visions; Case based reasoning References Repka, M.X. , Lum, F. , Burugapalli, B. Strabismus, Strabismus Surgery, and Reoperation Rate in the United States (2018) Ophthalmology, 125 (10) . Oct Chen, Z. , Fu, H. , Lo, W.-L. , Chi, Z. Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks (2018) J Healthc Eng, 2018. Li, S. , Tang, A. , Yang, B. , Wang, J. , Liu, L. Virtual reality-based vision therapy versus OBVAT in the treatment of convergence insufficiency, accommodative dysfunction: A pilot randomized controlled trial (2022) BMC Ophthalmol, 22 (1) . Dec Kanclerz, P. , Pluta, K. , Momeni-Moghaddam, H. , Khoramnia, R. Comparison of the Amplitude of Accommodation Measured Using a New-Generation Closed-Field Autorefractor with Conventional Subjective Methods (2022) Diagnostics, 12 (3) . Feb Ebrahimiadib, N. , Hassanpoor, N. , Niyousha, M. , Modjtahedi, B.S. The effect of scleral buckling on accommodative amplitude (2020) Int J Retina Vitreous, 6 (1) . Dec [https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&src=s&stateKey=OFD_1728191502&eid=2-s2.0-85175466425&sort=&clicked](https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&src=s&stateKey=OFD_1728191502&eid=2-s2.0-85175466425&sort=&clicked)… 1/3 12/26/23 , 2:56 PM Scopus-Print Document Burns, D.H. , Allen, P.M. , Edgar, D.F. , Evans, B.J.W. Sources of error in clinical measurement of the amplitude of accommodation (2020) J Optom , 13 (1), pp. 3-14. Jan Tong, Y. , Lu, W. , Yu, Y. , Shen, Y. Application of machine learning in ophthalmic imaging modalities (2020) Eye and Vision, 7 (1) . Dec Ahsan, M.M. , Luna, S.A. , Siddique, Z. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review Healthcare , 10 (3), p. 541. Mar. 2022 Kim, D. , Joo, J. , Zhu, G. , Seo, J. , Ha, J. , Kim, S.C. Strabismus Classification using Convolutional Neural Networks (2021) 20

FAQs

What problem does the study address?

The study addresses automating strabismus classification for binocular vision management, reducing misdiagnosis risk when clinicians have limited professional experience.

What machine learning approach is used for classification?

A case-based reasoning (CBR) machine learning algorithm is developed to automate strabismus classification.

How effective is the proposed method?

The reported results show 91.8% accuracy, 89.29% precision, 92.59% recall, and 90.91% F1-score.

Aug 5, 2026

Upload by Emma Wilson