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LMGU-NET: methodological intervention for prediction of bone health for clinical recommendations

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Abstract

Osteoporosis (OP) is a bone-related ailment that aggravates owing to the decline in bone mineral density (BMD) or during deviations in the structure or quality of bone that may surge to fractures. The low BMD can be recognized from computed tomography (CT), X-ray, or Dual Energy X-ray absorptiometry (DXA/DEXA). Texture analysis is the most significant and distinguishing image feature. An enhanced discrimination power texture feature extraction system is developed for volumetric images by combining two complementary types of information: local binary patterns (LBP) and normalized grey-level co-occurrence matrix-based (nGLCM) techniques to extract features and U-Net for classification. The developed algorithm was validated on a Kaggle dataset comprising X-ray images acquired from patients suffering from osteoporosis. The modified U-Net (ModU-Net) semantic segmentation classifier is used for segmenting the low bone mass sections in the processed image. The developed LGMU-Net algorithm outperforms conventional feature extraction approaches and neural networks with a Dice Score of 88.82%, Tanimoto Co-efficient index of 71.74%, MSE of 0.0321, and PSNR of 65.74 dB. This method assists physicians in making early diagnoses and also protects patients from bone fraility and eventual fractures by ensuring that they follow the medications/surgery options prescribed by the doctors.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank Dr.R.Sayee Venkatesh, M.D.(General Medicine), D.M.(Cardio), Chennai, Tamilnadu, India, for supporting the research. Also, the authors thank the International Research Centre of Kalasalingam Academy of Research and Education, Tamil Nadu, India, for permitting them to use the computational facilities available in the Biomedical Research and Diagnostic Techniques Development Centre. This research was supported by the Department of Science and Technology, New Delhi, under the Biomedical Device and Technology Development Scheme (BDTD) of the Technology Development Programme (TDP). (Ref. No. DST / TDP / BDTD / 28 / 2021 (G)).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Department of Science and Technology [DST/TDP/BDTD/28/2021(G)].

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India

    Gautam Amiya

  2. Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India

    Pallikonda Rajasekaran Murugan, Kottaimalai Ramaraj, Muneeswaran Vasudevan & S. Sheik Abdullah

  3. Data Science Division, School of Computer Science and Engineering, VIT Bhopal University, Madhya Pradesh, India

    Vishnuvarthanan Govindaraj & Arunprasath Thiyagarajan

  4. Department of Biomedical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India

    M. Thirumurugan

  5. Consultant Orthopaedic Surgeon, MGR Medical University, Chennai, Tamilnadu, India

    Gautam Amiya, Pallikonda Rajasekaran Murugan, Kottaimalai Ramaraj, Vishnuvarthanan Govindaraj, Muneeswaran Vasudevan, M. Thirumurugan, S. Sheik Abdullah & Arunprasath Thiyagarajan

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  1. Gautam Amiya
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Contributions

Conceptualization: Gautam Amiya; Data curation: Kottaimalai Ramaraj, Muneeswaran Vasudevan; Formal Analysis: Vishnuvarthanan Govindaraj; Investigation: Gautam Amiya; Methodology: Gautam Amiya; Project administration: Pallikonda Rajasekaran Murugan; Supervision: Pallikonda Rajasekaran Murugan, Thirumurugan M; Validation: Thirumurugan M, Sheik Abdullah S; Visualization: Arunprasath Thiyagarajan; Writing – original draft: Gautam Amiya; Writing – review and editing: Kottaimalai Ramaraj, Vishnuvarthanan Govindaraj.

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Correspondence to Pallikonda Rajasekaran Murugan or Vishnuvarthanan Govindaraj.

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Amiya, G., Murugan, P.R., Ramaraj, K. et al. LMGU-NET: methodological intervention for prediction of bone health for clinical recommendations. J Supercomput 80, 15636–15663 (2024). https://doi.org/10.1007/s11227-024-06048-2

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