## 6 Conclusions  
The proposed system offers a comprehensive approach to diabetic macular edema (DME) grading, addressing the critical need for early detection and classification of this condition. With diabetes becoming increasingly prevalent worldwide, the risk of DME-related vision loss emphasizes the urgency for accurate diagnostic tools. Leveraging advances in medical imaging and deep learning, our system integrates multiple modules to provide interpretable and clinically relevant DME grading. In the first module, our system employs ConvUNeXt to segment hard exudates (HaEx), a key indicator of DME, from retinal fundus images. Through rigorous experimentation and dataset comparisons, we demonstrate the effectiveness of our segmentation approach across diverse datasets, ensuring robust performance under varying conditions. After that, our system proceeds to localize the optic disc (OD), a crucial reference point for subsequent macula localization. Utilizing RetinaNet, we achieve precise OD localization, laying the groundwork for accurate macula area calculations. The macula localization module refines our system's capabilities by employing preprocessing techniques to enhance macula visibility and optimize image representations to facilitate accurate macula localization, which is crucial for DME severity assessment. In the final stage, our system consolidates the outcomes from all preceding modules to assign DME grades, providing practical insights for ophthalmologists. By integrating segmentation results, OD coordinates, and macula localization information, our system offers interpretable grading outcomes, facilitating informed clinical decision-making. Experimental results demonstrate the effectiveness of our system across multiple performance metrics, with competitive accuracy, sensitivity, and specificity compared to existing methods. While non-interpretable methods often excel in certain metrics, our system maintains the balance between performance and interpretability, offering valuable insights into the decision-making process. As a future work, we will consider adding retinal image quality assessment into the proposed algorithm to discard some low-quality images that make it difficult to perform accurate DME grading. **Author Contributions:** Conceptualization, Z.G.-N. and A.F.-R.; methodology, M.N. ; software, Z.G.-N.; validation, A.F.-R. and M.N. ; formal analysis, M.N. ; investigation, Z.G.-N. and A.F.-R.; resources, M.N. ; data curation, Z.G.-N.; writing--original draft preparation, Z.G.-N.; writing--review and editing, A.F.-R. and M.N. ; visualization, Z.G.-N.; supervision, A.F.-R. and M.N. ; project administration, M.N. ; funding acquisition, M.N. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by The Instituto Politecnico Nacional of Mexico with funding number 20230733 and 2024909. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. All images used belong to public databases. **Data Availability Statement:** The image databases used for this research are public databases, which are available in: Messidor Dataset: [[https://www.adcis.net/en/third-party/messidor2/](https://www.adcis.net/en/third-party/messidor2/)]([https://www.adcis.net/en/third-party/messidor2/](https://www.adcis.net/en/third-party/messidor2/)) (accessed on 22 June 2024), Retinal Lesions Dataset: [[https://www.kaggle.com/c/diabetic-retinopathy-detection](https://www.kaggle.com/c/diabetic-retinopathy-detection)]([https://www.kaggle.com/c/diabetic-retinopathy-detection](https://www.kaggle.com/c/diabetic-retinopathy-detection)) (accessed on 22 June 2024), DDR Dataset: [[https://github.com/nkicsl/DDR-dataset](https://github.com/nkicsl/DDR-dataset)]([https://github.com/nkicsl/DDR-dataset](https://github.com/nkicsl/DDR-dataset)) (accessed on 22 June 2024).