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Comparing stacking ensemble techniques to improve musculoskeletal fracture image classification
ID Kandel, Ibrahem (Author), ID Castelli, Mauro (Author), ID Popovič, Aleš (Author)

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Abstract
Bone fractures are among the main reasons for emergency room admittance and require a rapid response from doctors. Bone fractures can be severe and can lead to permanent disability if not treated correctly and rapidly. Using X-ray imaging in the emergency room to detect fractures is a challenging task that requires an experienced radiologist, a specialist who is not always available. The availability of an automatic tool for image classification can provide a second opinion for doctors operating in the emergency room and reduce the error rate in diagnosis. This study aims to increase the existing state-of-the-art convolutional neural networks' performance by using various ensemble techniques. In this approach, different CNNs (Convolutional Neural Networks) are used to classify the images; rather than choosing the best one, a stacking ensemble provides a more reliable and robust classifier. The ensemble model outperforms the results of individual CNNs by an average of 10%.

Language:English
Keywords:neuroscience, deep learning, image classification, stacking, ensemble learning, convolutional neural networks, transfer learning, medical images
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:EF - School of Economics and Business
Publication status in journal:Published
Article version:Publisher's version of article
Year:2021
Number of pages:Str. 1-24
Numbering:Vol. 7, iss. 6 (art. 100)
PID:20.500.12556/RUL-127770 This link opens in a new window
UDC:659.2:004
ISSN on article:2313-433X
DOI:10.3390/jimaging7060100 This link opens in a new window
COBISS.SI-ID:67727363 This link opens in a new window
Publication date in RUL:22.06.2021
Views:444
Downloads:138
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Record is a part of a journal

Title:Journal of imaging
Shortened title:J. imaging
Publisher:MDPI
ISSN:2313-433X
COBISS.SI-ID:525653017 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:21.06.2021

Secondary language

Language:Slovenian
Keywords:informatika, programiranje, prenos znanja, kognitivna znanost

Projects

Funder:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:DSAIPA/DS/0022/2018
Name:GADgET

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Funding programme:Raziskovalni program
Project number:P5-0410
Name:Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe

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