Sistem Pendeteksi Penyakit Kanker Kulit Menggunakan Convolutional Neural Network Arsitektur YOLOv8 Berbasis Website
DOI:
https://doi.org/10.61132/neptunus.v2i3.224Keywords:
Skin Cancer, YOLOv8, CNN, Skin Cancer Detection Application, RoboflowAbstract
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.
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