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Comparing stacking ensemble techniques to improve musculoskeletal fracture image classification [Elektronski vir]Kandel, Ibrahem ; Castelli, Mauro ; Popovič, AlešBone 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%.Source: Journal of imaging. - ISSN 2313-433X (Vol. 7, iss. 6 (art. 100), 2021, str. 1-24)Type of material - e-articlePublish date - 2021Language - englishCOBISS.SI-ID - 67727363
Author
Kandel, Ibrahem |
Castelli, Mauro |
Popovič, Aleš
Topics
informatika |
programiranje |
prenos znanja |
kognitivna znanost |
informatika |
programiranje |
prenos znanja |
neuroscience
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Database name | Field | Year |
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Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
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Kandel, Ibrahem | |
Castelli, Mauro | 51146 |
Popovič, Aleš | 24105 |
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