Radiologists can sometimes overlook fractures because they are difficult to spot. A vast number of studies of deep learning on images have provided interesting applications to medical data analysis, with significant improvements in image-based diagnosis. This study has leveraged YOLOv4 versions that are expected to detect fractures in the wrist bone. The rigorous testing of three levels showed that the YOLOv4-based architectures obtained significantly better results than the state-of- the-art method based on the U-Net model. Our method is evaluated on a public dataset containing over 20,000 X-ray images of wrist fractures to conduct the experiments. The YOLOv4 achieves an accuracy of 0.89871, recall of 0.89871, precision of 0.90369, and F1 of 0.89997, outperforming the U-Net model.
Tạp chí khoa học Trường Đại học Cần Thơ
Lầu 4, Nhà Điều Hành, Khu II, đường 3/2, P. Xuân Khánh, Q. Ninh Kiều, TP. Cần Thơ
Điện thoại: (0292) 3 872 157; Email: tapchidhct@ctu.edu.vn
Chương trình chạy tốt nhất trên trình duyệt IE 9+ & FF 16+, độ phân giải màn hình 1024x768 trở lên