Comparing stacking ensemble techniques to improve musculoskeletal fracture image classification

Kandel, Ibrahem (author), Castelli, Mauro (author), Popovič, Aleš (author)

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: Published
• Publication version: Version of Record
• Year: 2021
• Number of pages: Str. 1-24
• Numbering: Vol. 7, iss. 6 (art. 100)
• PID: 20.500.12556/RUL-127770
• UDC: 659.2:004
• ISSN on article: 2313-433X
• DOI: 10.3390/jimaging7060100
• COBISS.SI-ID: 67727363
• Publication date in RUL: 22.06.2021

Record is a part of a journal

• Title: Journal of imaging
• Shortened title: J. imaging
• Publisher: MDPI
• ISSN: 2313-433X
• COBISS.SI-ID: 525653017

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 - Slovenian Research Agency
• Funding programme: Raziskovalni program
• Project number: P5-0410
• Name: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe