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This study investigates the classification of images of spine X-ray into three groups: Normal, Scoliosis, and Spondylolisthesis, deep learning models improves with attention mechanisms. A labelled dataset of X-ray images was working, addressed with imbalances class through oversampling techniques. Pretrained convolutional neural network (CNN) models, including Xception, InceptionV3, and DenseNet, were fine-tuned for this categorised task. The combination of attention mechanisms enhanced interpretability of model and precision score. Working with the models, InceptionV3 achieved perfect accuracy, outperforming Xception and DenseNet. The findings insides the efficacy of attention-based deep learning approaches with potential applications in clinical diagnostics, in medical image classification, for spinal conditions.

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Deep Learning-Based Classification of Spine X-Ray Images Using Attention Mechanisms

  • 会议论文

  • 发表日期：

    2026年

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  • Asaram Pandurang Janwale,
  • Minal Dutta,
  • Savita Mohurle,
  • Vaduguru Venkata Ramya

摘要

This study investigates the classification of images of spine X-ray into three groups: Normal, Scoliosis, and Spondylolisthesis, deep learning models improves with attention mechanisms. A labelled dataset of X-ray images was working, addressed with imbalances class through oversampling techniques. Pretrained convolutional neural network (CNN) models, including Xception, InceptionV3, and DenseNet, were fine-tuned for this categorised task. The combination of attention mechanisms enhanced interpretability of model and precision score. Working with the models, InceptionV3 achieved perfect accuracy, outperforming Xception and DenseNet. The findings insides the efficacy of attention-based deep learning approaches with potential applications in clinical diagnostics, in medical image classification, for spinal conditions.