From: A three-stage, deep learning, ensemble approach for prognosis in patients with Parkinson’s disease
Method | MAPE | MAE | MSE | r | R2 |
---|---|---|---|---|---|
Ensemble approach | 18.36% (11.74%, 24.98%) | 4.70 (3.56, 5.84) | 34.53 (18.81, 50.25) | 0.84 | 0.71 |
DaTscan + Semi-quantitative + All ImageNet | 19.64% (12.09%, 27.18%) | 4.79 (3.65, 5.94) | 35.48 (20.11, 50.85) | 0.82 | 0.67 |
DaTscan + Semi-quantitative | 18.82% (11.32%, 26.31%) | 4.74 (3.36, 6.12) | 40.60 (14.59, 66.60) | 0.79 | 0.62 |
DaTscan + All ImageNet | 20.64% (11.50%, 29.79%) | 4.81 (3.45, 6.18) | 40.95 (19.94, 61.96) | 0.79 | 0.63 |
Semi-quantitative + All ImageNet | 18.82% (12.75%, 24.89%) | 4.83 (3.69, 5.96) | 35.57 (20.41, 50.73) | 0.82 | 0.67 |
DaTscan | 19.89% (12.48%, 27.30%) | 5.04 (3.78, 6.29) | 40.41 (22.09, 58.74) | 0.81 | 0.66 |
Semi-quantitative | 21.43% (13.76%, 29.09%) | 5.58 (4.13, 7.03) | 51.29 (27.25, 75.33) | 0.76 | 0.57 |
All ImageNet | 20.18% (13.76%, 26.59%) | 5.15 (3.90, 6.40) | 41.44 (20.15, 62.72) | 0.78 | 0.62 |
VGG16 | 21.67% (13.99%, 29.35%) | 5.54 (4.14, 6.94) | 49.31 (26.95, 71.67) | 0.75 | 0.56 |
ResNet50 | 19.70% (13.82%, 25.58%) | 5.32 (4.03, 6.61) | 44.14 (25.64, 62.65) | 0.79 | 0.62 |
DenseNet121 | 20.24% (13.04%, 27.45%) | 5.23 (3.90, 6.56) | 44.12 (23.23, 65.01) | 0.79 | 0.63 |
InceptionV3 | 21.89% (14.30%, 29.48%) | 5.47 (4.17, 6.76) | 45.89 (25.32, 66.46) | 0.76 | 0.58 |