Automated diabetic retinopathy classification using vision transformers on optical confocal microscopy images

In this paper, we employed the Vision Transformer architecture to effectively classify diabetic retinopathy (DR) in retinal images. Our dataset consisted of validated images, categorized into two classes: "normal" and "abnormal." The model demonstrated robust performance, achieving a training accuracy of 98.91% and a validation accuracy of 98.79%, alongside a commendable test accuracy of 100%. The training process spanned 30 epochs, during which the model exhibited consistent improvements in accuracy and reductions in loss. We further evaluated the model efficacy through a classification report, which revealed precision, recall, and F1-scores of 1.00 for both classes, indicating perfect classification performance on the test set. The results highlight the model potential for clinical applications in DR detection, ensuring its capability to assist healthcare professionals in diagnosing DR with high accuracy. Future work will focus on enhancing the model generalizability across diverse datasets and exploring the integration of additional clinical features.