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

Povzetek

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%.

Ključne besede

informatika;programiranje;prenos znanja;kognitivna znanost;neuroscience;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL EF - Ekonomska fakulteta
UDK: 659.2:004
COBISS: 67727363 Povezava se bo odprla v novem oknu
ISSN: 2313-433X
Št. ogledov: 308
Št. prenosov: 87
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: informatika;programiranje;prenos znanja;kognitivna znanost;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 1-24
Letnik: ǂVol. ǂ7
Zvezek: ǂiss. ǂ6 (art. 100)
Čas izdaje: 2021
DOI: 10.3390/jimaging7060100
ID: 13055809