The Osteoporosis Disease Diagnosis and Classification Using U-net Deep Learning Process

Authors

  • D. Thrivikrama Rao 1Hindustan Aeronautics Limited, Bengaluru, Karnataka, India 2Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
  • K. S. Ramesh Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
  • V. S. Ghali Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India https://orcid.org/0000-0003-4938-3893
  • M. Venugopala Rao Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India https://orcid.org/0000-0002-0189-1015

DOI:

https://doi.org/10.13052/jmm1550-4646.1848

Keywords:

Osteoporosis, U-Net, deep learning, classification

Abstract

The purpose of this research has been used to detect osteoporosis disease for Knee radiography. It can improve diagnostic performance over using the scan thermal image mode alone. During 2016 and 2021, researchers gathered CT, MRI, CTA, ultra sound images from individuals who had both skeletal bone density assessment and knee radiology at a local medical clinic for subjective labelling. But following models are most complicate to detect diagnosis of osteoporosis. Therefore, five level convolutional neural networks (CNN) models were used to diagnose osteoporosis from knee radiography. They also looked at ensemble models that included clinical variables in each U-Net. Every net was given an efficiency, accuracy, recall, sensitivity, negative predictive value (npv), F1 measure, and area under curve (AUC) rating. Exclusively knee rays were used to test the U-Net model, but GoogleNet, S-transform, ResNet and FCNN had the lowest accuracy, precision, and specificity. Whenever patient’s data were added, Efficient U-Net had the highest accuracy 99.23%, recall 98.76%, npv 0.93%, F1 score 99.23%, and AUC 99.72% scores among five level prediction methods. The U-Net models correctly identified osteoporosis from Knee radiography, and their performance had improved even more when clinical variables from health records were complex. This u-net based osteoporosis diagnosis is most helpful for future generation for better pre-detections.

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Author Biographies

D. Thrivikrama Rao, 1Hindustan Aeronautics Limited, Bengaluru, Karnataka, India 2Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

D. Thrivikrama Rao is working in Hindustan Aeronautics Limited, Bengaluru, Karnataka, India, pursuing Ph.D., Research scholar, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India. His research area is medical thermal image processing. He has 5 Scopus publications in various image related areas.

K. S. Ramesh, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

K. S. Ramesh, Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India. His research area is wireless communication and thermal image processing. He has 61 Scopus publications in various wireless and thermal image applications.

V. S. Ghali, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

V. S. Ghali, Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India. Ghali, Venkata Subbarao he has 71 Scopus publications and his research area is thermal image processing and thermal wave imaging.

M. Venugopala Rao, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

M. Venugopala Rao, Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India. He has 56 Scopus publications and his research area is micro wave applications.

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Published

2022-03-16

How to Cite

Rao, D. T. ., Ramesh, K. S. ., Ghali, V. S. ., & Rao, M. V. . (2022). The Osteoporosis Disease Diagnosis and Classification Using U-net Deep Learning Process. Journal of Mobile Multimedia, 18(04), 1131–1152. https://doi.org/10.13052/jmm1550-4646.1848

Issue

Section

Enabling AI Technologies Towards Multimedia Data Analytics for Smart Healthcare