Musculoskeletal images classification for detection of fractures using transfer learning

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

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.

• Jezik: Angleški jezik
• Ključne besede: informatika, programiranje, prenos znanja, neuroscience
• Vrsta gradiva: Članek v reviji (dk_c)
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: EF - Ekonomska fakulteta
• Leto izida: 2020
• Št. strani: str. 1-14
• Številčenje: Vol. 6, iss 11 (art. 127)
• UDK: 659.2:004
• ISSN pri članku: 2313-433X
• DOI: 10.3390/jimaging6110127
• COBISS.SI-ID: 39651587
• Licenca: ; To delo je dosegljivo pod licenco Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Skupna ocena: (0 glasov)
• Vaša ocena: Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.

Gradivo je del revije

• Naslov: Journal of imaging
• Skrajšan naslov: J. imaging
• Založnik: MDPI AG
• ISSN: 2313-433X
• COBISS.SI-ID: 525653017

Gradivo je financirano iz projekta

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
• Številka projekta: P5-0410
• Naslov: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe

• Financer: FCT - Fundação para a Ciência e a Tecnologia, I.P.
• Številka projekta: DSAIPA/DS/0022/2018
• Naslov: GADgET

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: informatika, programiranje, prenos znanja, kognitivna znanost