Automated Early Detection of Retinopathy of Prematurity Zones using SWIN Transformer

Retinopathy of prematurity (ROP) is known to be the primary cause leading to permanent vision loss in children, which calls for its diagnosis and treatment based on subjective assessment of retinal vascular characteristics; even though this traditional approach is practical, it takes much time and likely results in errors. Therefore, automation is required not only to enhance precision but also productivity. The study proposes an innovative approach to early detection of ROP zones on fundus images between 2015 and 2020. It will use the Swin Transformer model, which has demonstrated superior precision and achieved a performance rate of 90.11%. This work denotes significant advancement in this field, emphasizing the potential of transformer-based architectures for the precise and efficient detection of ROP in clinical environments. The findings underscore the significance of utilizing state-of-the-art, comprehensive learning approaches to enhance early detection procedures, improving clinical outcomes for at-risk newborns.