20.500.12556/RUL-124244 Musculoskeletal images classification for detection of fractures using transfer learning The classification of the musculoskeletal images can be very challenging, mostly when it is being done in the emergency room, where a decision must be made rapidly. The computer vision domain has gained increasing attention in recent years, due to its achievements in image classification. The convolutional neural network (CNN) is one of the latest computer vision algorithms that achieved state-of-the-art results. A CNN requires an enormous number of images to be adequately trained, and these are always scarce in the medical field. Transfer learning is a technique that is being used to train the CNN by using fewer images. In this paper, we study the appropriate method to classify musculoskeletal images by transfer learning and by training from scratch. We applied six state-of-the-art architectures and compared their performance with transfer learning and with a network trained from scratch. From our results, transfer learning did increase the model performance significantly, and, additionally, it made the model less prone to overfitting. transfer learning computer vision convolutional neural networks image classification musculoskeletal images deep learning medical images neuroscience informatika programiranje prenos znanja kognitivna znanost true false true Angleški jezik Slovenski jezik Članek v reviji 2021-01-12 14:59:31 2021-01-12 15:03:16 2022-09-04 03:58:37 0000-00-00 00:00:00 2020 0 0 14 str. iss. 11, art. 127 Vol. 6 Nov. 2020 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 659.2:004 2313-433X 10.3390/jimaging6110127 39651587 525653017 RAZ_Kandel_Ibrahem_2020.pdf RAZ_Kandel_Ibrahem_2020.pdf 1 F97E7AE6990CE43375CB7BAE50366BCF a7218e015854bbe679d8877ba03a935c0357cd2675055b43fb2b52c9f3f5c21c ad9d8b47-7ac1-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=153157 https://www.mdpi.com/2313-433X/6/11/127 0 74e01d36-7ac1-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=153156 Ekonomska fakulteta 0 0 0