Open Access

ARTICLE

Methodology for Detecting Strabismus through Video Analysis and Intelligent Mining Techniques

Hanan Abdullah Mengash^1,*, Hanan A. Hosni Mahmoud^1,2
1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 Department of Computer and Systems Engineering, Faculty of Engineering, University of Alexandria, Alexandria, Egypt
* Corresponding Author: Hanan Abdullah Mengash. Email:
(This article belongs to this Special Issue: Powering the Future Intelligence - Ambient Social Media Analytics)

Computers, Materials & Continua 2021, 67(1), 1013-1032. https://doi.org/10.32604/cmc.2021.014942

Received 28 October 2020; Accepted 20 November 2020; Issue published 12 January 2021

Abstract

Strabismus is a medical condition that is defined as the lack of coordination between the eyes. When Strabismus is detected at an early age, the chances of curing it are higher. The methods used to detect strabismus and measure its degree of deviation are complex and time-consuming, and they always require the presence of a physician. In this paper, we present a method of detecting strabismus and measuring its degree of deviation using videos of the patient’s eye region under a cover test. Our method involves extracting features from a set of training videos (training corpora) and using them to build a classifier. A decision tree (ID3) is built using labeled cases from actual strabismus diagnosis. Patterns are extracted from the corresponding videos of patients, and an association between the extracted features and actual diagnoses is established. Matching Rules from the correlation plot are used to predict diagnoses for future patients. The classifier was tested using a set of testing videos (testing corpora). The results showed 95.9% accuracy, 4.1% were light cases and could not be detected correctly from the videos, half of them were false positive and the other half was false negative.

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Keywords

Data mining; strabismus; video analysis; angle of deviation; decision tree

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