20.500.12556/RUL-127770 Comparing stacking ensemble techniques to improve musculoskeletal fracture image classification 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%. neuroscience deep learning image classification stacking ensemble learning convolutional neural networks transfer learning medical images informatika programiranje prenos znanja kognitivna znanost true false true Angleški jezik Slovenski jezik Članek v reviji 2021-06-22 11:32:02 2021-06-22 11:47:25 2022-09-06 03:53:02 0000-00-00 00:00:00 2021 0 0 Str. 1-24 iss. 6 (art. 100) Vol. 7 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 659.2:004 2313-433X 10.3390/jimaging7060100 67727363 RAZ_Kandel_Ibrahem_2021.pdf RAZ_Kandel_Ibrahem_2021.pdf 1 DBF9B6174C0AC646DE026F7905B526F1 827b489717829e561e6316a8e04ae23b9a29d782d12dcbe12f66c60fc28514ab 64f17343-d33e-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=144290 https://www.mdpi.com/2313-433X/7/6/100 0 6b69b001-d33e-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=144291 Ekonomska fakulteta 0 0 0