Pemodelan Deteksi dan Klasifikasi Fraktur Tulang pada Radiografi X-Ray Menggunakan YOLOv8 dan Preprocessing CLAHE - Hidayat, Jose Julian; Anshor, Abdul Halim; Anwar, M. Syaibani (Unversitas Muhammadiyah Riau, 2026)
This study aims to develop a model for detecting and classifying bone fractures in digital X-ray radiography images using the You Only Look Once version 8 (YOLOv8) architecture with the application of Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing method. The CLAHE method is used to improve contrast quality and clarify bone structure details, thereby facilitating the feature extraction process by the detection model. The research dataset comprises 641 X-ray and MRI images divided into ten classes consisting of various types of bone fractures, namely Comminuted, Greenstick, Linear, Oblique, Oblique Displaced, Segmental, Spiral, Transverse, and Transverse Displaced, as well as the Healthy class as a comparison. Model training was conducted for 100 epochs using YOLOv8n with CLAHE-based augmentation to improve the visibility of the fracture area. The best results were obtained from the YOLOv8-CLAHE (balanced) model with a mAP@0.5 of 0.933 to 0.941, precision of 0.939 to 0.965, and recall of 0.877 to 0.901. The Segmental and Comminuted classes showed the highest performance, while classes with limited data such as Greenstick and Linear still had relatively low accuracy. The model's inference speed reached 8.3 milliseconds per image, demonstrating the potential application of this system for real-time fracture detection in clinical settings. The results of this study show that the application of the CLAHE method in the image pre-processing stage can improve the detection and classification performance of YOLOv8, and has the potential to support the development of automated diagnosis systems in the field of orthopedic radiology.