InOsteoD: A Cost-effective Integrative Approach for Osteoporosis Detection

Authors

  • Parthajit Borah Department of Computer Science and Engineering, Tezpur University, Tezpur, 784028, Assam, India. Author
  • Rachayita Bhardwaj Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Delhi, 110016, New Delhi, India. Author
  • Pankaj Barah Department Molecular Biology and Biotechnology, Tezpur University, Tezpur, 784028, Assam, India. Author
  • Paramdeep Singh All India Institute of Medical Sciences, Dabwali Rd, Lal Singh Nagar, Bathinda, AIIMS, Punjab 151001, Punjab, India. Author
  • D.K. Bhattacharyya Department of Computer Science and Engineering, Tezpur University, Tezpur, 784028, Assam, India. Author

DOI:

https://doi.org/10.63635/mrj.v1i1.5

Keywords:

Osteoporosis, X-ray Deep learning, features;, classification

Abstract

Osteoporosis is a prevalent bone condition that typically affects older adults, occurring when bone mineral density and bone mass decline or when the bone’s quality or structure deteriorates. It increases the risk of bone fractures and causes many complications for patients. X-ray is a low-cost medical imaging test that can be used for predicting Osteoporosis by extracting relevant and non-redundant features followed by appropriate downstream analysis. This paper presents a cost-effective integrative approach called InOsteoD that exploits both deep learning as well as tree-based ensemble learning to detect Osteoporosis from X-ray images with high precision. The performance of InOsteoD has been found satisfactory on real-life datasets.

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Published

2025-03-31

Issue

Volume 1, Issue 1, 2025

Section

Research Articles

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How to Cite

Borah, P., Bhardwaj, R., Barah, P., Singh, P., & D.K, B. (2025). InOsteoD: A Cost-effective Integrative Approach for Osteoporosis Detection. Multidisciplinary Research Journal, 1(1), 26-41. https://doi.org/10.63635/mrj.v1i1.5

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