## 4 Results and Discussion
### Hardware Implementation Performance  
Figure 9 is an example of the output obtained by running the program on a single-board computer. Using the TensorFlow Lite file format, the model has a relatively small data size of 976 KB. TensorFlow Lite helps reduce the model size significantly from its original H5 format, which was 3 MB. The lightweight TensorFlow Lite model is well suited for implementation on low-cost computing devices such as those used in this paper. By using a lightweight model, the detection process becomes faster and produces good accuracy. In the first image, the model detects the melanoma class with a good accuracy of 99.88%. In the next image, the model detects the benign keratosis-like lesion class with a high accuracy of 97.42%. For the hardware implementation on single-board computers (SBCs), especially the Raspberry Pi 5 Model B and the NVIDIA Jetson Nano, the model produced excellent performance results, as shown in Table 4. Raspberry Pi achieved the fastest detection time \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Disease Class** & **Precision** & **Recall** & **F-1 Score** & **Support** \\ \hline akiec & 1.00 & 1.00 & 1.00 & 1667 \\ bcc & 0.99 & 1.00 & 0.99 & 1689 \\ bkl & 0.96 & 1.00 & 0.98 & 1651 \\ df & 1.00 & 1.00 & 1.00 & 1629 \\ nv & 0.99 & 0.89 & 0.94 & 1663 \\ vasc & 1.00 & 1.00 & 1.00 & 1680 \\ mel & 0.94 & 0.99 & 0.97 & 1755 \\ \hline Accuracy & & & 0.98 & 11734 \\ Macro avg. & 0.98 & 0.98 & 0.98 & 11734 \\ Weighted avg. & 0.98 & 0.98 & 0.98 & 11734 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Proposed model classification result. Figure 9: Model detection results on single-board computers. of 0.01 s, making it suitable for real-time applications, while Jetson Nano in MAXN mode demonstrated an efficient balance between speed and power consumption. Raspberry Pi consumed 4.825 W/h of power while utilizing 1948 MB of RAM, whereas Jetson Nano in MAXN power mode had a detection time of 0.029 s, with a power consumption of 3.385 W/h and RAM utilization of 1763 MB. In its low-power 5 Watt mode, the Jetson Nano had a slightly slower detection time of 0.048 s but consumed just 2.948 W/h, with a RAM utilization of 1960 MB. These results confirm that SBCs offer an effective balance between energy efficiency and real-time classification speed, making them ideal candidates for on-device AI applications in low-resource environments. However, implementing this strategy in places with restricted internet connectivity has additional obstacles. Because the proposed model runs purely on-device with TensorFlow Lite, it does not require constant internet access for inference. This makes it ideal for offline deployment in rural healthcare settings, where general practitioners or paramedics may utilize it as a decision support tool for early diagnosis. However, one significant shortcoming of this technique is that without access to centralized model updates, the model may become obsolete over time as more varied data become available. To overcome this issue in feature research, Federated Learning (FL) offers a possible solution. Instead of requiring centralized training, FL enables edge devices to train locally on fresh patient data and only send model updates (rather than raw patient data) when connection is available. This decentralized method protects patient privacy while allowing the model to develop over time. A realistic FL-based implementation would have the following workflow. A healthcare professional in a rural clinic utilizes the system for real-time inference, while the gadget also performs local training to fine-tune the model. When a healthcare professional visits a hospital or a central facility with internet connectivity, the trained model parameters (rather than raw patient data) are sent to a central server. The server aggregates multiple updates from different locations using techniques like FedAvg, improving overall model accuracy without compromising privacy [17].