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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

9781665469487

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

91-95

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

neuveden

Místo konání akce

Prague

Datum konání akce

13. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001070846300019