A Comparative Survey of Deep Learning Techniques for Spine X-Ray Condition Classification

Abstract

Spine-related disorders are common musculoskeletal conditions that can significantly affect mobility, posture, and quality of life. Spine X-ray imaging is widely used for the diagnosis and assessment of abnormalities such as scoliosis, spondylolisthesis, vertebral fractures, and degenerative changes. However, manual interpretation of radiographic images can be time-consuming and may be influenced by the experience of the radiologist. Recent advances in deep learning have provided effective approaches for automated analysis and classification of spine X-ray images. This paper presents a comparative survey of deep learning techniques used for spine X-ray condition classification. Various convolutional neural network (CNN) architectures and transfer-learning models, including ResNet, DenseNet, EfficientNet, and other advanced architectures, are reviewed based on their classification performance and applicability to different spinal conditions. The comparison considers important evaluation measures such as accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the curve (AUC), along with dataset characteristics and computational requirements. The survey also discusses the advantages and limitations of existing approaches, including class imbalance, limited dataset diversity, lack of external validation, and challenges in model interpretability. Furthermore, emerging approaches such as attention mechanisms and transformer-based architectures are examined for their potential to improve automated spinal abnormality classification. The findings indicate that deep learning has considerable potential for assisting clinical decision-making and reducing the workload associated with manual spine X-ray interpretation. Finally, the study identifies current research gaps and future directions toward robust, explainable, and clinically deployable automated spine X-ray classification systems.