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

• Main Authors: Jameel, Samer Kais, Aydin, Sezgin, Ghaeb, Nebras H, Majidpour, Jafar, Rashid, Tarik A, Salih, Sinan Q, JosephNg, P. S
• Format: Journal Article
• Language: English
• Published: 25-12-2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2301.11871

Biomolecules, 2022 Corneal diseases are the most common eye disorders. Deep learning techniques
are used to per-form 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.