Musculoskeletal images classification for detection of fractures using transfer learning
Kandel, Ibrahem (Author), Castelli, Mauro (Author), Popovič, Aleš (Author)

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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.

Keywords:informatika, programiranje, prenos znanja, neuroscience
Work type:Article (dk_c)
Tipology:1.01 - Original Scientific Article
Organization:EF - Faculty of Economics
Number of pages:str. 1-14
Numbering:Vol. 6, iss 11 (art. 127)
ISSN on article:2313-433X
DOI:10.3390/jimaging6110127 Link is opened in a new window
COBISS.SI-ID:39651587 Link is opened in a new window
License:CC BY 4.0
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
COBISS.SI-ID:525653017 This link opens in a new window

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

Secondary language

Keywords:informatika, programiranje, prenos znanja, kognitivna znanost

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