Table 2.
Classification performance of deep learning for ASD in the included studies
| Study | Model | TP | FP | FN | TN |
|---|---|---|---|---|---|
| Subah et al. [32] | AAL atlas | 68 | 4 | 12 | 90 |
| Subah et al. [32] | BASC atlas | 75 | 7 | 6 | 86 |
| Subah et al. [32] | CC200 Atlas | 74 | 9 | 7 | 85 |
| Subah et al. [32] | Power atlas | 69 | 3 | 11 | 90 |
| Alsaade et al. [16] | NASNetmobile | 121 | 35 | 29 | 115 |
| Alsaade et al. [16] | Xception | 127 | 36 | 23 | 114 |
| Alsaade et al. [16] | Xception | 129 | 40 | 21 | 110 |
| Ahammed et al. [33] | DarkASDNet | 227 | 16 | 9 | 220 |
| Lu et al. [34] | VGG16 | 112 | 8 | 3 | 107 |
| Zhang et al. [24] | AE + F-score | 357 | 130 | 148 | 400 |
| Wang et al. [35] | deepAutism | 423 | 57 | 33 | 274 |
| Nogay et al. [36] | DCNN | 938 | 0 | 0 | 893 |
| Alam et al. [23] | EfficientNetB0 | 136 | 19 | 14 | 131 |
| Alam et al. [23] | MobileNetV2 | 137 | 22 | 13 | 128 |
| Alam et al. [23] | ResNet50V2 | 135 | 16 | 15 | 134 |
| Alam et al. [23] | VGG19 | 132 | 16 | 18 | 134 |
| Alam et al. [23] | Xception | 138 | 12 | 12 | 138 |
| Saranya et al. [22] | FACS-CNN | 1534 | 133 | 133 | 1534 |
| Ahmed et al. [38] | MobileNet | 131 | 4 | 9 | 136 |
| Hasan et al. [39] | Kinematic-SWDA | 28 | 3 | 2 | 27 |