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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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Autores principales: Jameel, Samer Kais, Aydin, Sezgin, Ghaeb, Nebras H., Majidpour, Jafar, Rashid, Tarik A., Salih, Sinan Q., JosephNg, Poh Soon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775139/
https://www.ncbi.nlm.nih.gov/pubmed/36551316
http://dx.doi.org/10.3390/biom12121888
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author Jameel, Samer Kais
Aydin, Sezgin
Ghaeb, Nebras H.
Majidpour, Jafar
Rashid, Tarik A.
Salih, Sinan Q.
JosephNg, Poh Soon
author_facet Jameel, Samer Kais
Aydin, Sezgin
Ghaeb, Nebras H.
Majidpour, Jafar
Rashid, Tarik A.
Salih, Sinan Q.
JosephNg, Poh Soon
author_sort Jameel, Samer Kais
collection PubMed
description 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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spelling pubmed-97751392022-12-23 Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image Jameel, Samer Kais Aydin, Sezgin Ghaeb, Nebras H. Majidpour, Jafar Rashid, Tarik A. Salih, Sinan Q. JosephNg, Poh Soon Biomolecules Article 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. MDPI 2022-12-16 /pmc/articles/PMC9775139/ /pubmed/36551316 http://dx.doi.org/10.3390/biom12121888 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jameel, Samer Kais
Aydin, Sezgin
Ghaeb, Nebras H.
Majidpour, Jafar
Rashid, Tarik A.
Salih, Sinan Q.
JosephNg, Poh Soon
Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775139/
https://www.ncbi.nlm.nih.gov/pubmed/36551316
http://dx.doi.org/10.3390/biom12121888
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