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

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Abstract
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:transfer learning, computer vision, convolutional neural networks, image classification, musculoskeletal images, deep learning, medical images, neuroscience
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:2020
Number of pages:14 str.
Numbering:Vol. 6, iss. 11, art. 127
PID:20.500.12556/RUL-124244 This link opens in a new window
UDC:659.2:004
ISSN on article:2313-433X
DOI:10.3390/jimaging6110127 This link opens in a new window
COBISS.SI-ID:39651587 This link opens in a new window
Publication date in RUL:12.01.2021
Views:949
Downloads:271
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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:23.11.2020

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
Acronym:GADgET

Funder:ARRS - Slovenian Research Agency
Project number:P5-0410
Name:Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe

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