Machine Learning Techniques for Corneal Diseases Diagnosis: A Survey

Jameel, Samer Kais; Aydin, S.; Ghaeb, Nebras H.

doi:10.1142/s0219467821500169

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…A survey on artificial intelligence (AI) in ophthalmology and specifically in the area of keratoconus classification was conducted in 2021 by [23]. Another survey was performed in the same year by [24] which examined machine learning techniques for the diagnosis of corneal diseases. Last but not the last, research conducted in [25] led to the publication of a paper about sensitivity and specificity of Sirius indices in diagnosis of keratoconus and the detection of early-stage keratoconus.…”

Section: Issn: 2088-8708 mentioning

confidence: 99%

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

Priya

Shivanandappa²,

Devnath³

2023

IJECE

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<span lang="EN-US">The detection of keratoconus has been a difficult and arduous process over the years for ophthalmologists who have devised traditional approaches of diagnosis including the slit-lamp examination and observation of thinning of the corneal. The main contribution of this paper is using deep learning models namely Resnet50 and EfficientNet to not just detect whether an eye has been infected with keratoconus or not but also accurately detect the stages of infection namely mild, moderate, and advanced. The dataset used consists of corneal topographic maps and pentacam images. Individually the models achieved 97% and 94% accuracy on the dataset. We have also employed class activated maps (CAM) to observe and help visualize which areas of the images are utilized when making classifications for the different stages of keratoconus. Using deep learning models to predict the detection and severity of the infection can drastically speed up and provide accurate results at the same time.</span>

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Section: Issn: 2088-8708 mentioning

confidence: 99%

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

Priya

Shivanandappa²,

Devnath³

2023

IJECE

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“…The cornea, which protects the eye from external substances and helps to control visual focus, is stiff but very sensitive to touch [ 20 ]. There are many corneal disorders, for instance, bullous keratopathy, Cogan syndrome, corneal ulcer, herpes simplex keratitis, herpes zoster ophthalmicus, etc.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image

et al. 2022

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Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.

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Machine Learning Techniques for Corneal Diseases Diagnosis: A Survey

Cited by 2 publications

References 31 publications

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image

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