Deep learning by Vision Transformer to classify bacterial and fungal keratitis using different types of anterior segment images.

Journal: Computers in biology and medicine

PMID: 40107025

Abstract

PURPOSE: To develop three novel Vision Transformer (ViT) frameworks for the specific diagnosis of bacterial and fungal keratitis using different types of anterior segment images and compare their performances.

Authors

Yeo Kyoung Won

Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Choong Han Kim

Moonsoul Graduate School of Future Strategy, KAIST, Daejeon, Republic of Korea.
Jooyoung Jeon

Moonsoul Graduate School of Future Strategy, KAIST, Daejeon, Republic of Korea.
Jiho Cha

Moonsoul Graduate School of Future Strategy, KAIST, Daejeon, Republic of Korea.
Dong Hui Lim

Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea. Electronic address: ldhlse@gmail.com.

Keywords

Adult Aged Anterior Eye Segment Deep Learning Eye Infections, Bacterial Eye Infections, Fungal Female Humans Image Processing, Computer-Assisted Keratitis Male Middle Aged Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (40107025)

Deep learning by Vision Transformer to classify bacterial and fungal keratitis using different types of anterior segment images.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals