International Journal for Recent Development in Science and Technology

Traditional fundus imagebased diabetic retinopathy (DR) grading depends on the examiner's experience, requiring manual annotations on the fundus image and also being time-consuming.Wireless sensor networks (WSNs) combined with artificial intelligence (AI) technology can provide automatic decisionmaking for DR grading application.However, the diagnostic accuracy of the AI model is one of challenges that limited the effectiveness of the WSNs-aided DR grading application.Regarding this issue, we propose a WSN architecture and a parallel deep learning framework (HybridLG) for actualizing automatic DR grading and achieving a fundus imagebased deep learning model with superior classification performance, respectively.In particular, the framework constructs a convolutional neural network (CNN) backbone and a Transformer backbone in a parallel manner.A novel lightweight deep learning model named MobileViT-Plus is proposed to implement the Transformer backbone of the HybridLG, and a model training strategy inspired by an ensemble learning strategy is designed to improve the model generalization ability.Experimental results demonstrate the state-of-the-art performance of the proposed HybridLG framework, obtaining excellent performance in grading diabetic retinopathy with strong generalization performance.Our work is significant for guiding the studies of WSNs-aided DR grading and providing evidence for supporting the efficacy of the AI technology in DR grading applications.I.