Sistem Pendeteksi Penyakit Kanker Kulit Menggunakan Convolutional Neural Network Arsitektur YOLOv8 Berbasis Website

Abstract

Skin cancer has high incidence and fatality rates, making accurate and rapid detection crucial. This study developed a web-based skin cancer detection system using YOLOv8. The model detects seven types of skin cancer using a dataset of 3500 annotated images. Methods included data collection, pre-processing, augmentation, model training, and performance evaluation using precision, recall, and mean Average Precision (mAP). Results show that the YOLOv8 model achieved a precision of 0.975 and a recall of 0.969. Evaluation with a confusion matrix demonstrated strong detection capabilities. A web interface was developed to allow users to upload images and view detection results in real-time. The YOLOv8-based skin cancer detection system provides accurate results and can be used as a tool for early diagnosis.