## 5 Discussion  
WSNs-aided diabetic retinopathy grading can help clinicians to grade and diagnose retinopathy in real time. Specifically, the real-time monitoring system can help clinicians to diagnose the condition and take effective treatment measures in a timely manner, thus reducing the risk of further deterioration of the condition. Moreover, the WSNs-aided retinopathy grading management method can reduce the cost of treatment for diabetic patients. Through remote monitoring, clinicians can provide diagnosis and treatment recommendations to patients remotely, eliminating the need for patients to go to hospitals for complex examinations and treatments. By monitoring and analyzing the patient's eye condition in real time, doctors can determine the condition more precisely and adopt targeted treatment plans for different conditions. This personalized treatment approach can better meet the needs of different patients and improve treatment outcomes and patient satisfaction. To construct superior DR grading model based on deep learning technology for the WSN system, we propose a parallel deep learning framework (HybridLG) for actualizing automatic DR grading. We compared our proposed method with recent classification techniques and well-known networks. Our final integrated model outperforms these methods. In the early days of image recognition, traditional machine learning methods were utilized due to the absence of mainstream neural networks. However, the results were unsatisfactory, and ensemble learning was utilized to enhance accuracy rates. With the advent of deep learning, image classification algorithms shifted towards this methodology,  
Figure 6: The loss and ROC curves of five models for comparison. (**a**) Resnext101, (**b**) Se_resnet101, (**c**) Se_resnet50, (**d**) Sent154, and (**e**) MobileViT. and the progress in classification accuracy can be attributed to the advancement of deep learning techniques. Significant performance improvements have been achieved from simple models to deeper ResNet, attention mechanisms, and Transformer-related models. The HybridLG framework draws inspiration from old and new techniques, attempting to extract the best of both worlds. The proposed integrated model combines the strengths of CNN and Transformers, resulting in a more informative representation of image data without compromising the respective strengths of each algorithm. There is still scepticism surrounding proposals of frameworks in the medical field, primarily due to the perception of deep learning models as black boxes which acts as a deterrent for specialists. Medical choices impact the life of patients, and include risk and responsibility for the clinicians. In order to meet the requirement of a transparent and explainable AI, two main methods are pursued in eXplainable AI: transparent design methods and the post hoc explanation methods [56]. In the transparent design aspect, CNNs cannot use a coordinate system, which humans typically rely on to recognize shapes. Moreover, they cannot handle rotations of shapes. Conversely, the self-attention mechanism must utilize prior knowledge of an image's scale, translation invariance, and feature localization. As a result, it can only learn these features through large amounts of data. Consequently, the self-attentive mechanism can only establish accurate global relationships based on extensive data and may not perform as effectively as CNNs for small datasets. The HybridLG framework effectively combines the strengths of both models. In the post hoc explanation aspect, we conducted ablation experiments to prove that the effectiveness of the HybridLG framework. The HybridLG is a parallel training framework inspired by ensemble learning, which provides several benefits. Firstly, from a statistical perspective, learning tasks with large hypothesis spaces are more likely to suffer from poor generalization performance. A combination strategy can mitigate this risk by combining multiple models.