Musculoskeletal images classification for detection of fractures using transfer learning

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

Abstract

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.

Keywords

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

Data

Language:	English
Year of publishing:	2020
Typology:	1.01 - Original Scientific Article
Organization:	UL EF - Faculty of Economics
UDC:	659.2:004
COBISS:	39651587
ISSN:	2313-433X
Views:	463
Downloads:	208
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	informatika;programiranje;prenos znanja;kognitivna znanost;
Type (COBISS):	Article
Pages:	str. 1-14
Volume:	ǂVol. ǂ6
Issue:	ǂiss ǂ11 (art. 127)
Chronology:	Nov. 2020
DOI:	10.3390/jimaging6110127
ID:	12379728