Automatic Ocular Alignment Evaluation for Strabismus Detection Using U-NET and ResNet Networks

Simões, Thayane de Oliveira; Souza, Johnatan Carvalho; Almeida, João Dallyson Sousa de; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de

doi:10.1109/bracis.2019.00050

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The system proposed by [8] detects strabismus using convolutional neural networks; an eye tracker detects eye motion and feeds an image to the neural network. The authors in [9] proposed automatic ocular alignment for strabismus detection using U-NET networks. They devised an algorithm for computing the distance between the center of the iris center and corner of the eye with an accuracy of approximately 96%.…”

Section: Related Workmentioning

confidence: 99%

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Methodology for Detecting Strabismus through Video Analysis and Intelligent Mining Techniques

Mengash¹,

Mahmoud²

2021

Computers, Materials &Amp; Continua

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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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Section: Related Workmentioning

confidence: 99%

“…For the computerized image analysis, the Hirschberg test has also been performed, but it gives less precision. Eye-tracking techniques are also used for strabismus diagnosis through the analysis of gaze deviations [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Methodology for Detecting Strabismus through Video Analysis and Intelligent Mining Techniques

Mengash¹,

Mahmoud²

2021

Computers, Materials &Amp; Continua

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Medical Assistance for Detecting Cataract, Jaundice and Strabismus using Deep Learning

Garg,

Dhyani,

Nautiyal

et al. 2023

2023 IEEE Pune Section International Conference (PuneCon)

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A Comprehensive Review on the Diabetic Retinopathy, Glaucoma and Strabismus Detection Techniques Based on Machine Learning and Deep Learning

2022

EJMHS

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Diabetes is a condition in which a person’s body either does not respond to insulin supplied by their pancreas or does not create enough insulin. Diabetics are at a higher chance and risk of acquiring a variety of eye disorders over time. Early identification of eye diseases via an automated method has significant advantages over manual detection thanks to developments in machine learning techniques. Recently, some high research articles on the identification of eye diseases have been published. This paper will present a comprehensive survey of automated eye diseases detection systems which are Strabismus, Glaucoma, and Diabetic Retinopathy from a variety of perspectives, including (1) datasets that are available, (2) techniques of image preprocessing, and (3) deep learning models. The study offers a thorough overview of eye disease detection methods, including cutting-edge field methods, intending to provide vital insight into the research communities, all eye-related healthcare occupational, and diabetic patients.

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Automatic Ocular Alignment Evaluation for Strabismus Detection Using U-NET and ResNet Networks

Cited by 3 publications

References 14 publications

Methodology for Detecting Strabismus through Video Analysis and Intelligent Mining Techniques

Methodology for Detecting Strabismus through Video Analysis and Intelligent Mining Techniques

Medical Assistance for Detecting Cataract, Jaundice and Strabismus using Deep Learning

A Comprehensive Review on the Diabetic Retinopathy, Glaucoma and Strabismus Detection Techniques Based on Machine Learning and Deep Learning

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