Table 4 Performance of different models (TL and 3D) across the multiple classification tasks (binary, 3-way, 4-way).
Binary classification (AD vs CN) | ||||
|---|---|---|---|---|
Model | Time (SD) | Training acc. (± CI) | Validation acc. (±CI) | NMI |
InceptionV3 | 2.6 (0.5) | 100 | 76 ± 0.016 | 0.94 |
VGG16 | 20.7 (0.3) | 90 ± 0.006 | 70 ± 0.044 | 0.94 |
ResNet50 | 635 (0.2) | 100 | 89 ± 0.012 | 0.97 |
3D model | 108 (0.4) | 97 ± 0.012 | 86 ± 0.042 | 0.96 |
3-Way classification (AD vs CN vs DLB) | ||||
|---|---|---|---|---|
Model | Time (SD) | Training acc. (± CI) | Validation acc. (±CI) | NMI |
InceptionV3 | 28.2 (0.7) | 99.8 ± 0.0006 | 78 ± 0.019 | 0.61 |
VGG16 | 2.5 (0.2) | 89 ± 0.005 | 74 ± 0.025 | 0.6 |
ResNet50 | 877.3 (0.7) | 100 | 83 ± 0.044 | 0.79 |
3D model | 161 (0.3) | 96 ± 0.01 | 87 ±0.01 | 0.9 |
4-Way classification (AD vs CN vs DLB vs MCI) | ||||
|---|---|---|---|---|
Model | Time (SD) | Training acc. (±CI) | Validation acc. (± CI) | NMI |
InceptionV3 | 3.6 (0.6) | 97 ± 0.006 | 59 ± 0.01 | 0.57 |
VGG16 | 3.2 (0.1) | 69 ± 0.006 | 54 ± 0.019 | 0.56 |
ResNet50 | 1193 (0.8) | 100 | 66 ± 0.008 | 0.62 |
3D model | 296 (0.5) | 85 ± 0.026 | 73 ± 0.015 | 0.82 |