Towards Automated Keratoconus Screening Approach using Lateral Segment Photographed Images

Zaki, Wan Mimi Diyana Wan; Daud, Marizuana Mat; Saad, Assyareefah Hudaibah; Hussain, Aini; Mutalib, Haliza Abdul

doi:10.1109/iecbes48179.2021.9398781

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Zaki et al [ 22 ] used lateral segment photographed images (LSPI) which were extracted from videos of patients taken sideways with a smartphone camera. VGGNet 16 model is used for the classification achieving 95.75% accuracy, 92.25% sensitivity, and 99.25% specificity.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Keratoconus Classification with Convolutional Neural Networks Using Segmentation and Index Quantification of Eye Topography Images by Particle Swarm Optimisation

Subramanian

Ramesh

2022

BioMed Research International

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In keratoconus, the cornea assumes a conical shape due to its thinning and protrusion. Early detection of keratoconus is vital in preventing vision loss or costly repairs. In corneal topography maps, curvature and steepness can be distinguished by the colour scales, with warm colours representing curved steep areas and cold colours representing flat areas. With the advent of machine learning algorithms like convolutional neural networks (CNN), the identification and classification of keratoconus from these topography maps have been made faster and more accurate. The classification and grading of keratoconus depend on the colour scales used. Artefacts and minimal variations in the corneal shape, in mild or developing keratoconus, are not represented clearly in the image gradients. Segmentation of the maps needs to be carried out for identifying the severity of the keratoconus as well as for identifying the changes in the severity. In this paper, we are considering the use of particle swarm optimisation and its modifications for segmenting the topography image. Pretrained CNN models are then trained with the dataset and tested. Results show that the performance of the system in terms of accuracy is 95.9% compared to 93%, 95.3%, and 84% available in the literature for a 3-class classification that involved mild keratoconus or forme fruste keratoconus.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Keratoconus Classification with Convolutional Neural Networks Using Segmentation and Index Quantification of Eye Topography Images by Particle Swarm Optimisation

Subramanian

Ramesh

2022

BioMed Research International

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“…The use of mobile phones as disease-screening devices is promising [1]. Screening of anterior segment eye diseases by mobile phones from a direct photo of the eye has been successfully used in conjunctivitis [2], keratitis [3,4], dry eye disease [5], pterygium, a disease of the degenerated conjunctiva which overgrows on the cornea [6][7][8], and in keratoconus, with progressive thinning of the cone-shaped cornea [9]. A photograph of the eye can show the front part of the eye in detail, but since it is two-dimensional, it does not allow to display the depth.…”

Section: Introductionmentioning

confidence: 99%

Unexpected corneal reflection phenomenon alters smartphone 3D image-based models of the eye

Kurilová,

Bemberáková,

Kocián

et al. 2023

Journal of Electrical Engineering

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Reconstruction of a 3D eye model by photogrammetry from a smartphone video could be prospectively used in self-diagnosis, screening and telemedicine monitoring of diseases of the front part of the eye and its surroundings. The main use could be found in the treatment of diseases of the curvature and surface of the cornea and in follow-up after some refractive procedures. In our work, we create 3D image-based models of the eye after scanning the face with a smartphone. An unexpected phenomenon appeared during the reconstruction of the transparent cornea – a crater-like depression was formed at the place where nearby objects reflected on the cornea, which corresponds to the first Purkinje image, the so-called glint. We thus encountered complications that may arise when modelling transparent living structures from a video taken in a normal environment, which will need to be solved if we want to create such 3D models of the eye using this method for medical purposes. Another 3D reconstruction approach or additional algorithms must be considered as a future work.

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Comparative Analysis of Machine Learning Approaches for the Early Diagnosis of Keratoconus

Subramanian

Ramesh

Parameshachari

2022

Lecture Notes in Electrical Engineering

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Towards Automated Keratoconus Screening Approach using Lateral Segment Photographed Images

Cited by 5 publications

References 15 publications

[Retracted] Keratoconus Classification with Convolutional Neural Networks Using Segmentation and Index Quantification of Eye Topography Images by Particle Swarm Optimisation

[Retracted] Keratoconus Classification with Convolutional Neural Networks Using Segmentation and Index Quantification of Eye Topography Images by Particle Swarm Optimisation

Unexpected corneal reflection phenomenon alters smartphone 3D image-based models of the eye

Comparative Analysis of Machine Learning Approaches for the Early Diagnosis of Keratoconus

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