Wavelet-Attention Swin for Automatic Diabetic Retinopathy Classification

Abstract

Diabetic retinopathy (DR) is a complication of diabetes that affects the eyes by damaging the blood vessels in the retina. High blood sugar levels can cause leakage or blockage of these vessels, leading to vision loss or blindness. Early detection of DR is crucial to prevent blindness, but manually analyzing fundus images can be time-consuming, especially with a large number of images. Swin-Transformers have gained popularity in medical image analysis, reducing calculations and yielding improved results. This paper introduces the WT Attention-Db5 Block, which focuses attention on the high-frequency domain using Discrete Wavelet Transform (DWT). This block extracts detailed information from the high-frequency field while retaining essential low-frequency information. The study discusses findings from the 2019 Blindness Detection challenge (APTOS 2019 BD) held by the Asia Pacific Tele-Ophthalmology Society.The proposed WT-Swin model achieves significant improvements in classification accuracy. For Swin-T, the training and validation accuracies are 99.14% and 98.91%, respectively. For binary classification using Swin-B, the training accuracy is 99.01%, the validation accuracy is 99.18%, and the test accuracy is 98%. In multi-classification, the training and validation accuracies are 93.19% and 86.34%, respectively, while the test accuracy is 86%.In conclusion, early detection of DR is essential for preventing vision loss. The WT Attention-Db5 Block integrated into the WT-Swin model shows promising results in classification accuracy