USAGE OF ARTIFICIAL NEURAL NETWORKS IN THE DIAGNOSIS OF KNEE JOINT DISORDERS
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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:
References
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
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
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.
https://en.wikipedia.org/wiki/McNemar%27s_test
https://github.com/ahmedbesbes/mrnet
https://machinelearningmastery.com/mcnemars-test-for-machine-learning/
https://pytorch.org/vision/stable/models.html
https://stanfordmlgroup.github.io/competitions/mrnet/
https://www.mikulskibartosz.name/wilson-score-in-python-example/
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