Author(s):
Kandel, Ibrahem ; Castelli, Mauro ; Popovič, Aleš
Date: 2020
Persistent ID: http://hdl.handle.net/10362/117843
Origin: Repositório Institucional da UNL
Project/scholarship:
info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT;
Subject(s): Computer vision; Convolutional neural networks; Deep learning; Image classification; Medical images; Musculoskeletal images; Transfer learning; Radiology Nuclear Medicine and imaging; Computer Vision and Pattern Recognition; Computer Graphics and Computer-Aided Design; Electrical and Electronic Engineering
Description
Kandel, I., Castelli, M., & Popovič, A. (2020). Musculoskeletal images classification for detection of fractures using transfer learning. Journal of Imaging, 6(11), 1-14. [127]. https://doi.org/10.3390/jimaging6110127
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.
