An approach to the diagnosis of lumbar disc herniation using deep learning models - Prisilla, Ardha Ardea; Guo, Yue Leon; Jan, Yih-Kuen; Lin, Chih-Yang; Lin, Fu-Yu; Liau, Ben-Yi; Tsai, Jen-Yung; Ardhianto, Peter; Pusparani, Yori; Lung, Chi-Wen (Frontiers in Bioengineering and Biotechnology, 2023)
1. The YOLOv5x in the dataset with augmentation successfully performed the highest mAP compared to all YOLO models tested, which indicated the model with the highest performance model to detect lumbar disc herniation (LDH). 2. YOLOv5x without augmentation (non-AUG) dataset performed well in detecting LDH in L2-L3, L3-L4, L4-L5, and L5-S1 regions with values above 90%, which showed effectiveness in using a non-AUG dataset for training. 3. YOLOv5x showed the shortest training duration and the lightest weight compared to YOLOv6 and YOLOv7, indicating the most efficient LDH detection model.