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

Abstract 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.