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Tytuł pozycji:

Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

Tytuł:
Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
Autorzy:
Fatih Uysal
Fırat Hardalaç
Ozan Peker
Tolga Tolunay
Nil Tokgöz
Temat:
biomedical image classification
bone fractures
deep learning
ensemble learning
shoulder
transfer learning
Technology
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Źródło:
Applied Sciences, Vol 11, Iss 6, p 2723 (2021)
Wydawca:
MDPI AG, 2021.
Rok publikacji:
2021
Kolekcja:
LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
2076-3417
Relacje:
https://www.mdpi.com/2076-3417/11/6/2723; https://doaj.org/toc/2076-3417
DOI:
10.3390/app11062723
Dostęp URL:
https://doaj.org/article/389ead98109245fca1ebbf2ad001528c  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.389ead98109245fca1ebbf2ad001528c
Czasopismo naukowe
Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.

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