LGPR Publication Success in Scientific Reports by Dr. Narinder Kaur

#openaccess #Nature #ScientificReports #Q1 #SCI

Dr. Narinder Kaur, LGPR Candidate published in the Nature publishing Group's Scientific Reports.

Scientific Reports, 15, 41960 (2025) | https://www.nature.com/srep/

Impact Factor: 3.9

Cite Score: 6.7

Scientific Reports is an open access journal publishing original research from across all areas of the natural sciences, psychology, medicine and engineering.

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Transfer learning Based Osteoporosis Prediction Using Enhanced Medical Imaging and Fuzzy Fusion

https://doi.org/10.1038/s41598-025-25946-w

Narinder Kaur

Lincoln University College, Petaling Jaya, Malaysia.

Shakir Khan*

College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Ibtehal Alazman

Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Mona Bin-Asfour

Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Md Nasre Alam*

Department of Computer Science, Woldia University, Woldia, Ethiopia.

Vivekanandam Balasubramaniam

Lincoln University College, Petaling Jaya 47810, Malaysia

Pawan Whig

Vivekananda Institute of Professional Studies, Technical Campus, Pitampura, Delhi, India

Abstract

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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.

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