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

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

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Official URL: https://doi.org/10.3390/biom12121888


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.

Item Type: Article
Uncontrolled Keywords: conditional generative adversarial networks; corneal diseases; data augmentation; synthesize images; transfer learning.
Subjects: R Medicine > R Medicine (General)
R Medicine > RA Public aspects of medicine
R Medicine > RE Ophthalmology
Depositing User: Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited: 28 Dec 2022 08:41
Last Modified: 29 Dec 2022 03:06
URI: http://eprints.intimal.edu.my/id/eprint/1700

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