Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/4090 2018 2018 Department of Computer Science, Al-Imam Al-Adham University College, Baghdad, Iraq Nazar Nazar Department of Computer Science, Al-Imam Al-Adham University College, Baghdad, Iraq Bashar I. Hameed Department of Computer Science, Al-Imam Al-Adham University College, Baghdad, Iraq Humam K. Yaseen Biomedical Engineering Department, AL-Khwarizmi College of Engineering, Baghdad University, Baghdad, Iraq Nebras H. Ghaeb ATISP LAB, ENET'Com, University of Sfax, Sfax, Tunisia Mohamed Ksantini Retinopathy of prematurity (ROP) remains the leading cause of blindness in children. The detection and treatment of this disease mainly depend on subjective evaluation of the features of retinal blood vessels. This method is not only time-consuming but also prone to errors. The increasing number of such cases demands an urgent need for automated models to improve the accuracy and efficiency of diagnosis and treatment. This paper presents a method for early detection of ROP using the Swin Transformer, a hierarchical vision transformer architecture. This work focuses solely on the screening stages for ROP, as documented between 2015 and 2020, based on a dataset composed of 3720 retinal images from preterm infants, kindly made available by the Al-Amal Eye Center located in Baghdad, Iraq. The proposed model achieved a classification accuracy of 98.67% on a clinical ROP dataset. The results highlight the importance of the most recent in-depth learning methods in enhancing early detection techniques, ultimately leading to improved clinical outcomes for at-risk infants. 2026 2026 228 240 10.54216/FPA.210215 https://www.americaspg.com/articleinfo/3/show/4090