Utlizing sgans for generating synthetic images of pterygium: training future optometrists and ophthalmologists
Pterygium, an ocular surface disorder, poses diagnostic challenges for optometrists and ophthalmologists. Wepropose using Style-Generative Adversarial Networks (SGANs) to generate synthetic pterygium images fortraining purposes. A training dataset of 68 pterygium images collected during routine clin...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
2023
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/108842/7/108842_Utlizing%20sgans%20for%20generating%20synthetic%20images.pdf http://irep.iium.edu.my/108842/ https://journals.iium.edu.my/ijahs/index.php/IJAHS/article/view/826/726 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Pterygium, an ocular surface disorder, poses diagnostic challenges for optometrists and ophthalmologists. Wepropose using Style-Generative Adversarial Networks (SGANs) to generate synthetic pterygium images fortraining purposes. A training dataset of 68 pterygium images collected during routine clinical examinationswas used. Fréchet inception distance (FID) was employed to evaluate the similarity between the synthetic andoriginal images. FID analysis revealed that the synthetic images closely resemble the original pterygiumimages, suggesting a high degree of similarity. This indicates the potential of SGANs in generating realisticpterygium images. The successful generation of synthetic pterygium images using SGANs provides a valuabletool for training future optometrists and ophthalmologists in pterygium diagnosis and grading. By expandingthe availability of diverse pterygium images, trainees can enhance their skills and proficiency. The use ofsynthetic images overcomes limitations associated with obtaining a sufficient number of real pterygiumimages. Additionally, the availability of a large dataset of synthetic images enables the development ofadvanced machine learning algorithms and computer-assisted diagnostic tools, improving the accuracy andefficiency of pterygium grading. SGAN-generated images have the potential to standardize and control thetraining process, leading to improved patient care and management of pterygium |
|---|