USAGE OF ARTIFICIAL NEURAL NETWORKS IN THE DIAGNOSIS OF KNEE JOINT DISORDERS
Konrad Witkowski
k.l.p.witkowski@gmail.comSGH Warsaw School of Economics (Poland)
https://orcid.org/0009-0004-2916-8672
Mikołaj Wieczorek
Lublin University of Technology, Department of Electronics and Information Technology (Poland)
https://orcid.org/0000-0002-7879-9727
Abstract
Following article address the issue of automatic knee disorder diagnose with usage of neural networks. We proposed several hybrid neural net architectures which aim to successfully classify abnormality using MRI (magnetic resonance imaging) images acquired from publicly available dataset. To construct such combinations of models we used pretrained Alexnet, Resnet18 and Resnet34 downloaded from Torchvision. Experiments showed that for certain abnormalities our models can achieve up to 90% accuracy.
Keywords:
classification, MRI images, Resnet, AlexnetReferences
Bien N. et al.: Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med 15(11), 2018, e1002699 [http://doi.org/10.1371/journal.pmed.1002699].
DOI: https://doi.org/10.1371/journal.pmed.1002699
Google Scholar
He K., Zhang X., Ren S., Sun J.: Deep Residual Learning for Image Recognition. Computer Vision and Pattern Recognition 2015, arXiv:1512.03385.
DOI: https://doi.org/10.1109/CVPR.2016.90
Google Scholar
Krizhevsky A., Sutskever I., Hinton G. E.: ImageNet Classification with Deep Convolutional Neural Networks. F. Pereira, C. J. Burges, L. Bottou and K. Q. Weinberger: Advances in Neural Information Processing Systems 25 (NIPS 2012), 2012.
Google Scholar
https://en.wikipedia.org/wiki/McNemar%27s_test
Google Scholar
https://github.com/ahmedbesbes/mrnet
Google Scholar
https://machinelearningmastery.com/mcnemars-test-for-machine-learning/
Google Scholar
https://pytorch.org/vision/stable/models.html
Google Scholar
https://stanfordmlgroup.github.io/competitions/mrnet/
Google Scholar
https://www.mikulskibartosz.name/wilson-score-in-python-example/
Google Scholar
Authors
Konrad Witkowskik.l.p.witkowski@gmail.com
SGH Warsaw School of Economics Poland
https://orcid.org/0009-0004-2916-8672
Authors
Mikołaj WieczorekLublin University of Technology, Department of Electronics and Information Technology Poland
https://orcid.org/0000-0002-7879-9727
Statistics
Abstract views: 150PDF downloads: 204
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
