Affiliations 

  • 1 Computer Science Department, University of Raparin, Rania 46012, Iraq
  • 2 Department of Natural and Mathematical Sciences, Engineer Faculty, Tarsus University, Tarsus 33402, Turkey
  • 3 Biomedical Engineering Department, Al-Khawarezmi Eng. College, University of Baghdad, Baghdad 10011, Iraq
  • 4 Computer Science and Engineering Department, University of Kurdistan Hewlêr, Erbil 44001, Iraq
  • 5 Computer Science Department, Dijlah University College, Al-Dora, Baghdad 00964, Iraq
  • 6 Faculty of Data Science & Information Technology, INTI International University, Persiaran Perdana BBN, Nilai 71800, Negeri Sembilan, Malaysia
Biomolecules, 2022 Dec 16;12(12).
PMID: 36551316 DOI: 10.3390/biom12121888

Abstract

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.