Performance Analysis of Deep Neural Networks and YOLOv8n Algorithm in Early Detection of Skin Cancer

Convolutional Neural Networks (CNNs) can be used to detect and classify various types of skin cancers. This research undertakes a comprehensive comparison of YOLOv8n, ResNet50, VGG16, and MobileNetV2 on human skin cancer images. Each algorithm, distinguished by its unique architecture, is meticulously evaluated in terms of both time efficiency and accuracy. Notably, YOLOv8n emerges as the fastest among the tested models, demonstrating rapid processing capabilities. Conversely, ResNet50 stands out as the most accurate, showcasing superior performance in accurately classifying skin cancer lesions. This study contributes nuanced insights into the trade-offs between speed and precision, providing valuable guidance for selecting the most suitable algorithm based on specific requirements in the critical domain of skin cancer diagnosis.