## 5 Conclusions and Future Works  
In this paper, we used similarity learning to train an SNN and propose a DFU-Helper framework to assist doctors who diagnose and decide on the treatment protocol of DFU, and subsequently perform follow-up on a patient. We combined the CNN and ViT transformers and used transfer learning on the DFU dataset. As exposed in the limitations, there are several other models and techniques that can be further experimented with. Machine learning offers a set of techniques and methods that can turn raw data into realistic and tailored knowledge [60] which can, in turn, give additional insight to healthcare professionals. This tool must undergo testing in collaboration with healthcare experts who can formulate suitable protocols for its implementation within a cohort, as well as the collection of performance indicators. In our study, our intention was not to replace healthcare professionals but rather to offer an additional perspective through the utilization of deep learning and image similarity architecture. However, it is important to highlight that the tool represents a novelty in the realm of DFU management. Currently, the management heavily relies on the practitioner's experience, which, regrettably, can be susceptible to misinterpretation due to their workload. Incorporating DFU-Helper provides confirmation of their diagnoses, ultimately enhancing the reliability of the tool. Upon completing this phase, the ultimate objective would be to package the same functionality into a mobile application, enabling patients to engage in self-monitoring. The prompt detection and efficient management of diabetic foot ulcers can play a crucial role in preventing the advancement of wounds and reducing the risk of amputations. Our research has the potential for further development as a tool for early detection of DFUs, as demonstrated by our experimental findings with the addition of the new Healthy Class. An essential aspect of future work in this field is involving healthcare institutions in the data collection process, as data remains crucial for model development. This will necessitate further research into data confidentiality within the context of machine learning, and exploring federated learning models could be a potential avenue. Secondly, the incorporation of explainability into the system is imperative. Subsequent research needs to focus on ensuring that the output of DFU-Helper is interpretable, thus gaining acceptance from medical professionals. This avenue opens up the realm of Explainable Artificial Intelligence (XAI), an area that has gained significant momentum in recent times. The medium-term goal remains the initial deployment for doctors, with the ultimate aim of creating a patient application. This approach is aligned with the research conducted by [PERSON] et al. [61], which emphasizes the utilization of such tools in collaboration with healthcare experts. M.S.A.T. and S.D. : Conducted the research, authored the research paper, created figures, designed algorithms, implemented and tested code, acquired datasets, and executed experiments. They also participated in the analysis of the results. M.H. and K.T. : Provided research supervision, contributed to research design, guided the paper's structure, proofread the research paper, offered guidance for journal submission, participated in algorithm decision-making, and conducted critical analysis of the results. They also closely supervised the paper reviewing which is an essential part of the research. A.K.D. and V.P. : Contributed in the implementation part and participated in algorithm design. D.S. : Supervised the overall research and provided guidance for submission. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The research was funded by XLIM, UMR CNRS 7252, University of Limoges. Not applicable.