Automated Classification of Lumbar Spine Degenerative Conditions Using Convolutional Neural Networks

This paper presents convolutional neural networks (CNN) models for the automatic classification of lumbar spine degenerative conditions including lumbar spine degeneration (LSD), Spider, Tseg, and OSF-Other Spine Features. Using a tagged dataset derived from Kaggle, the effort aims to automate medical picture interpretation thereby enhancing diagnosis accuracy and efficiency. The proposed CNN architecture consists in several convolutional and pooling layers; next are fully linked layers, thereby capturing and learning relevant features. Performance is defined by metrics like accuracy, recall, f1- score, and precision; a confusion matrix provides visualizing capacity for misclassification identification and classification results. The model displays a notable degree of accuracy in identifying lumbar spine illnesses, therefore demonstrating the prospects of deep learning in medical diagnostics. Apart from supporting clinicians in diagnosis, this automated technology aims to reduce radiologist workload and improve patient outcomes.