Diabetic and Hypertensive Retinopathy Classification from Retinal Images Using Dual Vision Transformer

Accurate and automated classification of diabetic and hypertensive retinopathy conditions plays an important role in the diagnosis as it can lead to severe vision loss if not promptly diagnosed and treated. We propose to use a multi-scale method via dual vision transformer to improve the detection and classification of these retinopathy conditions. The method is capable of extracting more holistic global view of semantic feature and learning finer local features. To demonstrate the effectiveness of the method in this work, comparative experiments against the state-of-the-art methods are conducted. Experimental results show that the method in this work achieves better performance in terms of various metrics including accuracy, F1 score, precision, and recall. This work underscores the potential of employing multi-scale dual vision transformer method to enhance the reliability and precision of automated retinal disease classification, offering significant benefits for early diagnosis and patient care.