## 6 Result and Discussion
### Discussion  
Compared to the other baseline models, our suggested model exhibits improved vascular connection and fewer instances of breakage. The enhanced preservation of vessel continuity and more robust vessel segmentation are made possible by the integrated attention mechanisms, contextual understanding, and multi-scale feature fusion, which are responsible for this improvement. The superior performance of our proposed model on all the datasets demonstrates its strong generalization capabilities. Achieving consistently high metrics across these two distinct datasets indicates the model's robustness and potential applicability to various retinal imaging conditions and populations.  
Transformers are known for their computational intensity due to their self-attention mechanisms, which scale quadratically with the input size. This section discusses the computational requirements and potential limitations of our proposed model. The proposed retinal vessel segmentation model, integrating CBAM, Transformer, and JPU modules, has demonstrated superior performance but at a computational cost. The Transformer module, in particular, involves multi-head self-attention and positional encoding, which increase the model's complexity. To manage these computational demands, we utilized GPUs for training and inference.  
The implementation was carried out on an NVIDIA RTX 3090 GPU, with training times averaging 10 h for 50 epochs on the DRIVE dataset. Memory usage peaked at 18 GB due to the Transformer module's high-dimensional matrix operations. In terms of scalability, while the model performs well on standard datasets, applying it to higher-resolution images or real-time applications may require optimization strategies. These could include model pruning, quantization, or efficient transformer variants like Linformer or Performer, which aim to reduce the quadratic complexity of self-attention. Future work will focus on optimizing the model for real-time applications and deploying it on edge devices. This will involve reducing the model size and inference time without significantly compromising accuracy.  
The improved segmentation performance of our model has significant clinical implications in ophthalmology. Accurate retinal vessel segmentation is crucial for diagnosing and monitoring various eye conditions, including diabetic retinopathy, glaucoma, and hypertensive retinopathy. Improved segmentation facilitates early detection of microaneurysms, hemorrhages, and neovascularization, enabling timely intervention. Precise vessel segmentation allows clinicians to monitor disease progression more accurately, adjusting treatments as necessary. The model's high accuracy reduces the need for manual annotation, speeding up the diagnostic process and allowing clinicians to focus on treatment. Furthermore, Enhanced segmentation supports remote diagnosis, which is particularly beneficial in under-resourced regions, improving accessibility to eye care. We plan to collaborate with ophthalmologists to validate the model's performance in clinical settings, assessing its impact on diagnostic accuracy and patient outcomes. This collaboration will help refine the model based on real-world feedback, ensuring its effectiveness in diverse clinical scenarios.