Osteo-Net: A Robust Deep Learning-Based Diagnosis of Osteoporosis Using X-ray images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU147861" target="_blank" >RIV/00216305:26220/22:PU147861 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9851342" target="_blank" >https://ieeexplore.ieee.org/document/9851342</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP55681.2022.9851342" target="_blank" >10.1109/TSP55681.2022.9851342</a>

Result language

angličtina

Original language name

Osteo-Net: A Robust Deep Learning-Based Diagnosis of Osteoporosis Using X-ray images

Original language description

Osteoporosis results in the deterioration of bone tissues and this problem is prevalent among people all over the world, especially older people. The diagnosis of osteoporosis is frequently made clinically manifested by fractures linked with bone fragility. Thus, early diagnosis would be required to take proper treatment and eliminate excess fractures, lowering mortality and morbidity. The development of deep learning-based technique for diagnosing osteoporosis disease from bone X-ray images, a commonly available and low-cost image-based medical examination approach, is the objective of this study. A deep learning-based architecture, Osteo-Net with many blocks and skip connections is presented in this work. The proposed technique utilizes the robustness of deep learning models to extract high-level features from low-quality X-ray images. The trained model achieved a validation accuracy of 84.06% and testing accuracy of 82.61% on unseen test images with less training time. The proposed method is low-cost and computationally efficient. The experimental results show an excellent classification performance when used for osteoporosis screening and the high efficacy of the proposed method over other state-of-the-art methods. The proposed low-cost deep neural network-based approach could be utilized as a supplement to Dual-energy X-ray Absorptiometry (DXA) screening, particularly in primary health care centers with insufficient DXA machines.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20203 - Telecommunications

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

TSP 2022: 2022 45th International Conference on Telecommunications and Signal Processing

ISBN

9781665469487

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

91-95

Publisher name

Institute of Electrical and Electronics Engineers Inc.

Place of publication

neuveden

Event location

Prague

Event date

Jul 13, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001070846300019