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<p>Osteoporosis is a chronic condition affecting the bones, resulting in decreased bone density. It poses significant health risks, particularly for the elderly. Conventional diagnostic methods frequently lack precision and are time-consuming. This article presents FuzzyBoneNet, an innovative approach for predicting osteoporosis with transfer learning and enhanced medical imaging techniques. To improve X-ray images, we propose utilizing advanced image enhancement techniques, including top-hat/bottom-hat filtering and bilateral image improvement. We employ a set of transfer learning models like AlexNet, VGG-19, and Xception that coupled with a fuzzy rank-based fusion technique to enhance classification accuracy. Oversampling resolves class imbalance, while quantitative criteria such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) assess image quality. Research demonstrates that FuzzyBoneNet significantly outperforms existing leading approaches, accurately recognizing 98.68% of instances of normal, osteopenic, and osteoporotic bone conditions. The integration of deep learning with fuzzy logic may enhance the accuracy of osteoporosis detection, as demonstrated by this work.</p>

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Transfer learning based osteoporosis prediction using enhanced medical imaging and fuzzy fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月23日

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  • Narinder Kaur,
  • Shakir Khan,
  • Ibtehal Alazman,
  • Mona Bin-Asfour,
  • Md Nasre Alam,
  • Vivekanandam Balasubramaniam,
  • Pawan Whig

摘要

Osteoporosis is a chronic condition affecting the bones, resulting in decreased bone density. It poses significant health risks, particularly for the elderly. Conventional diagnostic methods frequently lack precision and are time-consuming. This article presents FuzzyBoneNet, an innovative approach for predicting osteoporosis with transfer learning and enhanced medical imaging techniques. To improve X-ray images, we propose utilizing advanced image enhancement techniques, including top-hat/bottom-hat filtering and bilateral image improvement. We employ a set of transfer learning models like AlexNet, VGG-19, and Xception that coupled with a fuzzy rank-based fusion technique to enhance classification accuracy. Oversampling resolves class imbalance, while quantitative criteria such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) assess image quality. Research demonstrates that FuzzyBoneNet significantly outperforms existing leading approaches, accurately recognizing 98.68% of instances of normal, osteopenic, and osteoporotic bone conditions. The integration of deep learning with fuzzy logic may enhance the accuracy of osteoporosis detection, as demonstrated by this work.