Real-Time Skin Disease Detection and Classification Using YOLOv8 Object Detection for Healthcare Diagnosis

Skin diseases are one of the most extensive and difficult to manage topics in healthcare, affecting millions of people worldwide. Current manual diagnosis by healthcare professionals is time-consuming and dependent on an individual’s experience, authority or a medical professional’s subjective opinion, so it is paramount to create new methods to evaluate the severity of skin damage. Deep learning and computer vision technologies focused on automating the process of diagnosing and classifying skin diseases are among the most promising areas. This paper investigates the usage of webcam video streams for real-time detection and categorization of 35 different skin conditions using the YOLOv8 object detection platform. The focus will be on gathering a large and diverse dataset with annotations from Labelme and training the YOLOv8m to accurately identify and delimit regions of interest to be classified later. Key components include the setup of the Anaconda environment, installation of necessary dependencies, dataset preparation, model configuration, training procedure, and evaluation metrics. Real-time inference using webcam feed facilitates continuous monitoring and detection, providing a practical tool for dermatologists and healthcare professionals.