Strabismus Classification using Convolutional Neural Networks

Kim, Dong Hwan; Joo, Jaehan; Guohua, Zhu; Seo, Jeongbin; Ha, Jaeseung; Kim, Suk Chan

doi:10.1109/icaiic51459.2021.9415280

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Given that there is no publicly available dataset of strabismus gaze images, Jonathan Santos [24] proposed increasing the data of the strabismus dataset with a small amount of data through the deep convolutional confrontation network (DCGAN). Kim [25] used a CNN to classify a normal gaze image and a strabismus gaze image, and the test accuracy was 66.7%. In Ce Zheng's study [26] based on gaze images of 5218 patients (3021 normal patients and 2197 strabismus patients), the classification training on VGG16 and Inception-v3 achieved very good results, and the test accuracy was 0.95.…”

Section: Application Of Deep Learning Models In Strabismus Detectionmentioning

confidence: 99%

Multi‐feature fusion‐based strabismus detection for children

Zhang

Xu²,

Gong³

et al. 2023

IET Image Processing

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Strabismus is a common ophthalmologic disease that affects approximately 1.19% to 5.0% of children; however if the disease is detected early it can be treated effectively. Generally, the automatic detection of strabismus is usually performed only by a single feature, which is, with image deep features or ratio features. However, the accuracy of a strabismus diagnosis with a single feature is unreliable. This study aims to develop an intelligent strabismus detection model driven by corneal light reflection photographs to automatically detect children's strabismus. The proposed multi‐feature fusion model (MFFM) improves the detection performance by fusing the deep features and ratio features extracted from the corneal light reflection photographs to identify strabismus. The experimental results demonstrate that our proposed multi‐feature model outperforms all of the single feature models in strabismus detection. The experiments show that the proposed method achieves an accuracy of 97.17%, sensitivity of 96.06%, specificity of 97.79%, and AUC of 0.969 in strabismus detection. Our evidence shows that it greatly improves the performance of strabismus detection.

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Section: Application Of Deep Learning Models In Strabismus Detectionmentioning

confidence: 99%

Multi‐feature fusion‐based strabismus detection for children

Zhang

Xu²,

Gong³

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

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Fake Data Generation for Medical Image Augmentation using GANs

Kim

Joo

Kim

2022

2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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An Automated Strabismus Classification Using Machine Learning Algorithm for Binocular Vision Management System

Isyraf Rohismadi,

Mat Raffei,

Akmar Zulkifli

et al. 2023

2023 IEEE 8th International Conference on Software Engineering and Computer Systems (ICSECS)

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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.

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Strabismus Classification using Convolutional Neural Networks

Cited by 3 publications

References 3 publications

Multi‐feature fusion‐based strabismus detection for children

Multi‐feature fusion‐based strabismus detection for children

Fake Data Generation for Medical Image Augmentation using GANs

An Automated Strabismus Classification Using Machine Learning Algorithm for Binocular Vision Management System

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