A Hybrid Artificial Intelligence Model for Detecting Keratoconus

Alyasseri, Zaid Abdi Alkareem; Al-Timemy, Ali H.; Abasi, Ammar Kamal; Lavric, Alexandru; Mohammed, Husam Jasim; Takahashi, Hidenori; Filho, Jose Arthur Milhomens; Campos, Mauro; Hazarbassanov, Rossen Mihaylov; Yousefi, Siamak

doi:10.3390/app122412979

Cited by 2 publications

(1 citation statement)

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“…Machine learning (ML) algorithms [4] have been used as a basis for creating several different automated models, including support vector machines (SVM) [5], decision trees [6], and neural networks [7], which hold promise for the early detection and identification of subclinical forms of KCN, as well as established KCN. These methods are typically combined with hand crafted features [8] or machine generated features from corneal topography.…”

Section: Introductionmentioning

confidence: 99%

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

et al. 2023

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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.

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Section: Introductionmentioning

confidence: 99%

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

et al. 2023

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Artificial intelligence for detecting keratoconus

Vandevenne,

Favuzza,

Veta

et al. 2023

Cochrane Database of Systematic Reviews

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A Hybrid Artificial Intelligence Model for Detecting Keratoconus

Cited by 2 publications

References 44 publications

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

Artificial intelligence for detecting keratoconus

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