Advancing medical imaging diagnostics using deep learning for accurate spinal disorder classification.

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Abstract

Advancements in medical imaging and Deep Learning (DL) have significantly improved the diagnosis of spinal disorders. Traditional diagnostic methods often depend on manual interpretation by radiologists, which is time-consuming and susceptible to human error. Existing automated approaches face limitations such as small datasets, inconsistent image quality, and difficulty in distinguishing between visually similar spinal conditions. To address these issues, this study introduces Spinal Disorder Classification using Deep Learning (SDC-DL), a DL-based framework for spinal disorder classification using convolutional neural networks (CNNs). SDC-DL incorporates transfer learning, advanced data augmentation, and fine-tuning to enhance feature extraction and improve model robustness across variable imaging conditions. The model is trained on a large dataset comprising spinal X-rays and Magnetic Resonance Imaging (MRI) scans, enabling it to accurately classify a wide range of spinal disorders. Experimental results demonstrate that SDC-DL outperforms existing methods in terms of accuracy, sensitivity, and specificity. By reducing the dependence on manual analysis and addressing the limitations of current systems, SDC-DL presents a scalable and reliable solution that supports faster and more accurate clinical decision-making in spinal diagnostics. The SDC-DL framework significantly enhances spinal disorder diagnosis accuracy using DL. It shows strong clinical potential, though further work is needed to improve generalizability and handle data imbalance.

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