Comparing stacking ensemble techniques to improve musculoskeletal fracture image classification

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

Bone fractures are among the main reasons for emergency room admittance and require a rapid response from doctors. Bone fractures can be severe and can lead to permanent disability if not treated correctly and rapidly. Using X-ray imaging in the emergency room to detect fractures is a challenging task that requires an experienced radiologist, a specialist who is not always available. The availability of an automatic tool for image classification can provide a second opinion for doctors operating in the emergency room and reduce the error rate in diagnosis. This study aims to increase the existing state-of-the-art convolutional neural networks' performance by using various ensemble techniques. In this approach, different CNNs (Convolutional Neural Networks) are used to classify the images; rather than choosing the best one, a stacking ensemble provides a more reliable and robust classifier. The ensemble model outperforms the results of individual CNNs by an average of 10%.

• Jezik: Angleški jezik
• Ključne besede: neuroscience, deep learning, image classification, stacking, ensemble learning, convolutional neural networks, transfer learning, medical images
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: EF - Ekonomska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2021
• Št. strani: Str. 1-24
• Številčenje: Vol. 7, iss. 6 (art. 100)
• PID: 20.500.12556/RUL-127770
• UDK: 659.2:004
• ISSN pri članku: 2313-433X
• DOI: 10.3390/jimaging7060100
• COBISS.SI-ID: 67727363
• Datum objave v RUL: 22.06.2021

Gradivo je del revije

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

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 21.06.2021

Sekundarni jezik

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

Projekti

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

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Program financ.: Raziskovalni program
• Številka projekta: P5-0410
• Naslov: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe