Performance Evaluation of Convolutional Neural Network (CNN) for Skin Cancer Detection on Edge Computing Devices

Skin cancer is one of the most common and life-threatening diseases. In the current era, early detection remains a significant challenge, particularly in remote and underserved regions with limited internet access. Traditional skin cancer detection systems often depend on image classification using deep learning models that require constant connectivity to internet access, creating barriers in areas with poor infrastructure. To address this limitation, CNN provides an innovative solution by enabling on-device machine learning on low-computing Internet of Things (IoT) devices. This study evaluates the performance of a convolutional neural network (CNN) model trained on 10,000 dermoscopic images spanning seven classes from the Harvard Skin Lesion dataset. Unlike previous research, which seldom offers detailed performance evaluations on IoT hardware, this work benchmarks the CNN model on multiple single-board computers (SBCs), including low-computing devices like Raspberry Pi and Jetson Nano. The evaluation focuses on classification accuracy and hardware efficiency, analyzing the impact of varying training dataset sizes to assess the model's scalability and effectiveness on resource-constrained devices. The simulation results demonstrate the feasibility of deploying accurate and efficient skin cancer detection systems directly on low-power hardware. The simulation results show that our proposed method achieves an accuracy of 98.25%, with the fastest hardware being the Raspberry Pi 5, which achieves a detection time of 0.01 s.