SKIN CANCER CLASSIFICATION USING YOLO V5 AND IoT

In this study, we introduce an innovative approach for skin cancer classification utilizing the YOLOv5 architecture and IoT technology.Skin cancer remains a pressing public health issue, with early detection being pivotal for favorable patient outcomes.Our model aims to advance the accuracy and efficiency of skin cancer diagnosis by leveraging YOLOv5's object detection capabilities and IoT's real-time data processing.By integrating YOLOv5's robust detection with IoT's connectivity, our model demonstrates superior performance in identifying malignant and benign skin lesions.We assess our model on extensive dermatoscopic image datasets, showcasing its efficacy in accurately classifying various types of skin cancer lesions.Our proposed approach shows potential in enabling early diagnosis and enhancing treatment outcomes for skin cancer patients through real-time monitoring and intervention facilitated by IoT technology.