Retinopathy is characterized by pathological alterations to the retina that can lead to partial or total vision loss. It can result from a variety of causes, including diabetes,hypertension, and autoimmune disorders, such as Autoimmune Retinopathy, which is particularly challenging to detect and diagnose. Due to its visually similar features, Autoimmune Retinopathy is often mistaken for other retinal diseases that require distinct treatments. The objective of this work is to develop an anomaly detection system for retinal images using Transformer-based deep learning models. By leveraging Transformers, the model captures subtlevariations in retinal images, enhancing diagnostic accuracy. This approach aims to assist ophthalmologists in precise disease detection, improving early diagnosis and enabling personalized treatment strategies.