Utilizing SGANs for generating synthetic images of pterygium: training future optometrists and ophthalmologists
Pterygium, an ocular surface disorder, poses diagnostic challenges for optometrists and ophthalmologists. We propose using Style-Generative Adversarial Networks (SGANs) to generate synthetic pterygium images for training purposes. A training dataset of 68 pterygium images collected during routine...
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| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
IIUM Press
2023
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/108839/1/108839_Utilizing%20SGANs%20for%20generating.pdf http://irep.iium.edu.my/108839/ https://journals.iium.edu.my/ijahs/index.php/IJAHS/article/view/826 |
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| Summary: | Pterygium, an ocular surface disorder, poses diagnostic challenges for optometrists and ophthalmologists. We
propose using Style-Generative Adversarial Networks (SGANs) to generate synthetic pterygium images for
training purposes. A training dataset of 68 pterygium images collected during routine clinical examinations
was used. Fréchet inception distance (FID) was employed to evaluate the similarity between the synthetic and
original images. FID analysis revealed that the synthetic images closely resemble the original pterygium
images, suggesting a high degree of similarity. This indicates the potential of SGANs in generating realistic
pterygium images. The successful generation of synthetic pterygium images using SGANs provides a valuable
tool for training future optometrists and ophthalmologists in pterygium diagnosis and grading. By expanding
the availability of diverse pterygium images, trainees can enhance their skills and proficiency. The use of
synthetic images overcomes limitations associated with obtaining a sufficient number of real pterygium
images. Additionally, the availability of a large dataset of synthetic images enables the development of
advanced machine learning algorithms and computer-assisted diagnostic tools, improving the accuracy and
efficiency of pterygium grading. SGAN-generated images have the potential to standardize and control the
training process, leading to improved patient care and management of pterygium. |
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