A Wireless Sensor System for Diabetic Retinopathy Grading Using MobileViT-Plus and ResNet-Based Hybrid Deep Learning Framework

Traditional fundus image-based 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 decision-making 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 image-based 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.

Automated summary

Traditional fundus image-based diabetic retinopathy (DR) grading is time-consuming and depends on the examiner's experience. Wireless sensor networks (WSNs) combined with artificial intelligence (AI) can provide automatic decision-making for DR grading application. Our proposed WSN architecture and parallel deep learning framework (HybridLG) achieve automatic DR grading with superior classification performance, addressing the challenge of diagnostic accuracy in the AI model. This work guides WSNs-aided DR grading studies and supports the efficacy of AI technology in DR grading applications.