Automated Lumbar Spine Object Detection

Abstract

Accurate analysis of lumbar spine X-ray images is critical for early diagnosis and treatment of spinal conditions. This research leverages YOLOv8, a state-of-the-art object detection model, to automate the detection and localization of lumbar spine abnormalities. By fine-tuning YOLOv8 on a custom lumbar spine dataset, the model achieves high detection accuracy with real-time performance. The methodology includes data augmentation, model training, evaluation, and visualization of results. This study demonstrates the effectiveness of deep learning in enhancing radiological workflows and improving diagnostic accuracy in medical imaging.A custom-labelled lumbar spine X-ray dataset was curated and used to fine-tune the YOLOv8 model. The methodology involved rigorous data preprocessing, targeted data augmentation strategies, model training under transfer learning protocols, and comprehensive evaluation using standard object detection metrics such as Precision, Recall, and mean Average Precision (mAP). The results demonstrate that the fine-tuned model achieves a mAP50 score of 91.5%, with a precision of 92% and recall of 89%, outperforming conventional CNN-based approaches in both detection accuracy and speed. Visualizations of the model's outputs confirm its ability to accurately localize vertebrae, identify degenerative changes, and flag abnormal regions of interest. These findings underscore the potential of YOLOv8 as a reliable and efficient tool to assist radiologists, enhance clinical workflows, and improve diagnostic accuracy, particularly in resource-constrained healthcare environments. Future work will explore integration with segmentation models and broader validation across diverse patient demographics to further solidify the system's clinical applicability.