Automatic Defect Detection of Spine Vertebrate using Deep Learning

This abstract presents a novel approach to spine defect detection utilizing Convolutional Neural Networks (CNNs) and image augmentation techniques. The study focuses on enhancing the accuracy and reliability of defect identification in spinal structures through the application of advanced machine learning models. By leveraging CNNs, the model is capable of analyzing high-resolution images of spinal structures to identify and classify defects with high precision. The incorporation of image augmentation strategies significantly improves the model's robustness against variations in image quality and orientation, thereby ensuring more consistent and reliable detection outcomes. The proposed model achieves state of the art performance in terms of accuracy, precision, recall, and F1 score, demonstrating its potential to significantly contribute to the field of medical imaging and diagnostics. This research contributes to the development of more effective and efficient tools for early detection and diagnosis of spinal defects, ultimately supporting improved patient care and outcomes.