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: 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 in journal: Published
• Article version: Publisher's version of article
• Year: 2020
• Number of pages: 14 str.
• Numbering: Vol. 6, iss. 11, art. 127
• PID: 20.500.12556/RUL-124244
• UDC: 659.2:004
• ISSN on article: 2313-433X
• DOI: 10.3390/jimaging6110127
• COBISS.SI-ID: 39651587
• Publication date in RUL: 12.01.2021

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: 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 - Agencija za raziskovalno dejavnost Republike Slovenije
• Project number: P5-0410
• Name: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe