Table 2 Performances of the model in diagnosing myelopathy

Model details

Input image size 224 × 224

Data augmentation (used the zoom, width, and shear function)

Binary classification with sigmoid activation

Adam optimizer (the initial learning rate of 10⁻⁵)

Batch size 8

Performance

Training accuracy: 92.4%

Test accuracy: 87.1%

Test recall: 86.4%

Test precision: 88.9%

Test AUC: 0.864 with 95% CI [0.780–0.949]