Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image
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 usin...
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Molecular Diversity Preservation International (MDPI)
2022
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my-inti-eprints.17002022-12-29T03:06:19Z http://eprints.intimal.edu.my/1700/ Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image Samer Kais, Jameel Sezgin, Aydin Nebras H., Ghaeb Jafar, Majidpour Tarik A., Rashid Sinan Q., Salih Ng, Joseph, Poh Soon R Medicine (General) RA Public aspects of medicine RE Ophthalmology 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. Molecular Diversity Preservation International (MDPI) 2022-12 Article PeerReviewed Samer Kais, Jameel and Sezgin, Aydin and Nebras H., Ghaeb and Jafar, Majidpour and Tarik A., Rashid and Sinan Q., Salih and Ng, Joseph, Poh Soon (2022) Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image. Biomolecules, 12 (12). ISSN 2218-273X https://doi.org/10.3390/biom12121888 |
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R Medicine (General) RA Public aspects of medicine RE Ophthalmology Samer Kais, Jameel Sezgin, Aydin Nebras H., Ghaeb Jafar, Majidpour Tarik A., Rashid Sinan Q., Salih Ng, Joseph, Poh Soon Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image |
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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. |
format |
Article |
author |
Samer Kais, Jameel Sezgin, Aydin Nebras H., Ghaeb Jafar, Majidpour Tarik A., Rashid Sinan Q., Salih Ng, Joseph, Poh Soon |
author_facet |
Samer Kais, Jameel Sezgin, Aydin Nebras H., Ghaeb Jafar, Majidpour Tarik A., Rashid Sinan Q., Salih Ng, Joseph, Poh Soon |
author_sort |
Samer Kais, Jameel |
title |
Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image |
title_short |
Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image |
title_full |
Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image |
title_fullStr |
Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image |
title_full_unstemmed |
Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image |
title_sort |
exploiting the generative adversarial network approach to create a synthetic topography corneal image |
publisher |
Molecular Diversity Preservation International (MDPI) |
publishDate |
2022 |
url |
http://eprints.intimal.edu.my/1700/ https://doi.org/10.3390/biom12121888 |
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1753793390340734976 |
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13.188404 |