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

Abstract

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%.

Keywords

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

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL EF - Faculty of Economics
UDC: 659.2:004
COBISS: 67727363 Link will open in a new window
ISSN: 2313-433X
Views: 308
Downloads: 87
Average score: 0 (0 votes)
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Other data

Secondary language: Slovenian
Secondary keywords: informatika;programiranje;prenos znanja;kognitivna znanost;
Type (COBISS): Article
Pages: str. 1-24
Volume: ǂVol. ǂ7
Issue: ǂiss. ǂ6 (art. 100)
Chronology: 2021
DOI: 10.3390/jimaging7060100
ID: 13055809