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

TL;DR

This paper presents a method using conditional GANs to synthesize corneal topography images, addressing data scarcity, balancing datasets, and improving CNN diagnostic performance for corneal diseases.

Contribution

It introduces a novel CGAN-based approach for generating synthetic corneal images to enhance medical datasets and improve diagnosis accuracy.

Findings

Synthetic images are useful for medical diagnosis.

Balanced datasets improve CNN performance.

Generated images help identify disease severity.

Abstract

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.