Musculoskeletal images classification for detection of fractures using transfer learning

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

The classification of the musculoskeletal images can be very challenging, mostly when it is being done in the emergency room, where a decision must be made rapidly. The computer vision domain has gained increasing attention in recent years, due to its achievements in image classification. The convolutional neural network (CNN) is one of the latest computer vision algorithms that achieved state-of-the-art results. A CNN requires an enormous number of images to be adequately trained, and these are always scarce in the medical field. Transfer learning is a technique that is being used to train the CNN by using fewer images. In this paper, we study the appropriate method to classify musculoskeletal images by transfer learning and by training from scratch. We applied six state-of-the-art architectures and compared their performance with transfer learning and with a network trained from scratch. From our results, transfer learning did increase the model performance significantly, and, additionally, it made the model less prone to overfitting.

• Language: English
• Keywords: informatika, programiranje, prenos znanja, neuroscience
• Work type: Article (dk_c)
• Tipology: 1.01 - Original Scientific Article
• Organization: EF - Faculty of Economics
• Year: 2020
• Number of pages: str. 1-14
• Numbering: Vol. 6, iss 11 (art. 127)
• UDC: 659.2:004
• ISSN on article: 2313-433X
• DOI: 10.3390/jimaging6110127
• COBISS.SI-ID: 39651587
• License: ; This work is available under this license: Creative Commons Attribution 4.0 International
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Record is a part of a journal

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

Document is financed by a project

• Funder: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
• Project no.: P5-0410
• Name: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe

• Funder: FCT - Fundação para a Ciência e a Tecnologia, I.P.
• Project no.: DSAIPA/DS/0022/2018
• Name: GADgET

Secondary language

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