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©The Author(s) 2022.
World J Orthop. Jun 18, 2022; 13(6): 603-614
Published online Jun 18, 2022. doi: 10.5312/wjo.v13.i6.603
Published online Jun 18, 2022. doi: 10.5312/wjo.v13.i6.603
Model name | Accuracy | Precision | Recall | Loss | Outcome |
ResNet50 | 54.29% | 61.03% | 39.52% | 1.06 | Average |
VGG-16 | 56.68% | 67.56% | 35.02% | 1.10 | Average |
InceptionV3 | 87.34% | 89.19% | 85.67% | 0.35 | Good |
MobilnetV2 | 82.15% | 84.66% | 80.21% | 0.46 | Average |
EfficientnetB7 | 56.61% | 70.09% | 38.27% | 0.98 | Average |
DenseNet201 | 92.87% | 93.69% | 92.53% | 0.20 | Best |
Xception | 82.81% | 85.03% | 77.05% | 0.50 | Average |
NasNetMobile | 80.90% | 83.98% | 77.30% | 0.50 | Average |
Surgeon | 74.22% | 79.50% | 50.00% | 0.25 | Good |
- Citation: Tiwari A, Poduval M, Bagaria V. Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs. World J Orthop 2022; 13(6): 603-614
- URL: https://www.wjgnet.com/2218-5836/full/v13/i6/603.htm
- DOI: https://dx.doi.org/10.5312/wjo.v13.i6.603