## 1 Introduction  
With millions of cases recorded each year, skin cancer ranks as one of the most prevalent and potentially deadly diseases impacting individuals worldwide. Early identification is essential for enhancing survival rates, as it allows for prompt medical intervention and care. Dermoscopy of skin cancer, a non-invasive technique, has evolved into a crucial instrument for diagnosing skin lesions by offering intricate visual representations of the skin's surface. Nevertheless, evaluating these images correctly demands considerable expertise, which is frequently scarce in remote or resource-limited areas.  
In recent years, progress in artificial intelligence (AI) has resulted in the creation of automated systems for detecting skin cancer. Convolutional neural networks (CNNs) have shown exceptional effectiveness in tasks related to image classification, particularly in medical imaging fields. CNNs are highly effective at extracting features, recognizing patterns, and classifying skin lesion images into different types like melanoma, benign nevi,and keratosis. For example, the HAM10000 dataset, comprising 10,015 dermoscopic images of skin cancer, has been widely utilized in training CNN models to reach high classification precision.  
Figure 1 depicts the development flow model for implementing deep learning on single-board computers, which provides a formal framework for maximizing CNN performance on resource-constrained IoT devices. The integration of deep learning models into IoT devices is a rapidly expanding topic, notably in healthcare. Raspberry Pi (Raspberry Pi Ltd., Cambridge, UK) and NVIDIA Jetson Nano (Nvidia, Santa Clara, CA, USA) are affordable platforms for delivering CNN-based models in medical imaging. CNNs excel in feature extraction and image classification, making them ideal for skin lesion analysis. However, installing CNNs on low-power IoT devices raises issues with computational efficiency, memory utilization, and energy consumption. This study assesses a CNN model tailored for embedded systems using CNN and compares its classification accuracy and hardware performance on Raspberry Pi and Jetson Nano. The objective is to provide skin cancer diagnosis in resource-constrained areas without using cloud computing.  
Although CNNs are very efficient, implementing them on low-computing IoT devices poses difficulties related to resource limitations, including memory, computational power, and energy use. This study tackles these problems by assessing the effectiveness of a model utilizing CNN. The research contrasts the classification accuracy and hardware efficiency of the model on Raspberry Pi and Jetson Nano, providing insights into the feasibility of skin cancer detection in resource-limited settings.  
The primary contributions of this research include analysis for the proposed deep learning model and architecture for skin cancer detection, benchmarking CNN performance on single-board computers for skin cancer detection and assessing hardware efficiency in terms of processing speed, memory usage, and power consumption. The findings seek to close the gap between cutting-edge deep learning models and their practical  
Figure 1: Flow development model for deep learning on a single-board computers.  
implementation in low-computing environments, so that cancer can be tackled more effectively, especially in areas with limited infrastructure.  
The rest of this article is organized as follows. Section 2 discusses related works relevant to this research. Section 3 describes the materials and methods, including dataset preprocessing, model architecture, performance metric evaluation, and single-board computer platforms. Section 4 describes the paper results and discussion, including a model performance evaluation and hardware implementation performance. Finally, Section 5 provides the conclusions and future research.