## 5 Conclusions  
The HAM10000 dataset was used in this study to create and test a convolutional neural network (CNN) model for skin cancer classification on single-board computer (SBC) devices. The technique included data preprocessing, picture modification with UV spot detection and pigment comparison, model training and assessment, and hardware implementation. The suggested CNN model obtained 98% accuracy, beating other state-of-the-art techniques. Performance criteria including accuracy, recall, and F1-score showed consistent reliability across all seven illness categories.  
Experiments demonstrated that the model performed well when implemented on SBCs like Raspberry Pi 5 Model B and NVIDIA Jetson Nano. Raspberry Pi had the quickest detection time of 0.01 s, whereas Jetson Nano, especially in its MAXN power mode, combined efficient power use with a detection time of 0.036 s. Jetson Nano's low-power 5 W mode, which operates at 3.1 W/h, has proven practical for energy-efficient applications.  
These findings demonstrate the viability of using the CNN model in real-time, portable skin cancer detection devices with little hardware. Furthermore, the findings identify areas for further improvement in practical healthcare diagnostic applications.  
Future research should improve the model's adaptation to low-connectivity conditions. While the system functions offline, it does not receive constant updates, which may reduce its usefulness. Federated Learning (FL) is a promising method that allows for on-device training from remote places while syncing updates when internet connectivity is available. FL enables edge devices such as Raspberry Pi and Jetson Nano to learn on localized data while maintaining patient privacy. This strategy improves model performance without transferring raw medical data. However, issues including communication efficiency, resource restrictions, and model heterogeneity must be addressed.  
Furthermore, using Explainable AI (XAI) can increase model interpretability, which boosts confidence among healthcare practitioners. Further optimization for real-time deployment on edge devices is required for broader use, guaranteeing scalable, privacy-preserving, and efficient skin cancer detection across a variety of medical scenarios.  
Conceptualization, N.S.; Methodology, V. and N.S.; Software, V.; validation, V., G.D. and N.S.; Investigation, V., G.D. and N.S.; Resources, N.S.; Data curation, V. and G.D.; Writing-original draft preparation, V., G.D. and N.S.; Writing--review and editing, V., G.D. and N.S.; visualization, V., G.D. and N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the Harvard Dataverse at HAM10000, reference number [15]. These data were derived from the following resources available in the public domain: [[https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T)]([https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T)), accessed date 1 January 2025.  
The authors declare no conflicts of interest.