diabetic retinopathy detection from retinal images using the pyramid vision transformer method

The development of automated diagnostic tools is essential for efficiently analyzing medical data, particularly for conditions like diabetic retinopathy, a leading cause of vision loss and blindness in adults. the asia pacific tele-ophthalmology society 2019 blindness detection dataset, which includes detailed retinal images, is crucial for the advancement of these tools. this study focuses on using the pyramid vision transformer to improve both the accuracy and efficiency of diabetic retinopathy detection. unlike the traditional vision transformer, which is computationally expensive and produces low-resolution outputs due to its single-scale structure, pyramid’s multi-scale architecture enables more efficient feature representation. this design allows for better management of large feature maps and enhances image resolution, both vital for precise diagnoses. by implementing the pyramid vision transformer, our approach not only increases accuracy but also improves resource efficiency, outperforming conventional convolutional neural networks. extensive experiments demonstrate that the model significantly boosts detection and classification accuracy, making it a valuable tool for clinical applications. the model achieved 92.38% accuracy and an area under the curve of 99.58%. these results highlight the model’s effectiveness in real-world applications. future research will focus on optimizing the model for even better performance and exploring its clinical integration to further enhance the diagnostic process in healthcare.