Table 7.
This table summarises the results acquired from the fivefold stratified CV.
Balanced accuracy | Macro precision | Macro recall | Macro F1 score | Kappa score | Inference time (s) | Training time (h) | |
---|---|---|---|---|---|---|---|
EfficientNetB0 | 0.9303 ± 0.0322 | 0.9161 ± 0.0408 | 0.9303 ± 0.0322 | 0.9211 ± 0.0378 | 0.9180 ± 0.0362 | 0.0810 ± 0.0006 | 0.5565 ± 0.0088 |
EfficientNetV2B0 | 0.9076 ± 0.0398 | 0.8988 ± 0.0429 | 0.9076 ± 0.0398 | 0.9000 ± 0.0416 | 0.9040 ± 0.0455 | 0.0753 ± 0.0004 | 0.5630 ± 0.0287 |
EfficientNetV2B0-21k | 0.9666 ± 0.0185 | 0.9646 ± 0.0174 | 0.9666 ± 0.0185 | 0.9642 ± 0.0184 | 0.9678 ± 0.0154 | 0.0758 ± 0.0001 | 0.5592 ± 0.0162 |
ResNetV1-50 | 0.9253 ± 0.0310 | 0.9244 ± 0.0358 | 0.9253 ± 0.0310 | 0.9206 ± 0.0334 | 0.9255 ± 0.0313 | 0.2184 ± 0.0014 | 0.5795 ± 0.0556 |
ResNetV2-50 | 0.9346 ± 0.0156 | 0.9199 ± 0.0276 | 0.9346 ± 0.0156 | 0.9259 ± 0.0202 | 0.9233 ± 0.0247 | 0.2277 ± 0.0010 | 0.5968 ± 0.0478 |
MobileNetV1 | 0.9518 ± 0.0232 | 0.9526 ± 0.0180 | 0.9518 ± 0.0232 | 0.9506 ± 0.0214 | 0.9543 ± 0.0181 | 0.0424 ± 0.0004 | 0.5628 ± 0.0340 |
MobileNetV2 | 0.9362 ± 0.0322 | 0.9339 ± 0.0251 | 0.9362 ± 0.0322 | 0.9314 ± 0.0305 | 0.9357 ± 0.0278 | 0.0456 ± 0.0011 | 0.5659 ± 0.0818 |
Each performance metric was reported in average (± standard deviation) form. The bold values represent the best score in each category. The best overall performing model was found to be EfficientNetV2-B0-21k, and the fastest CNN model was MobileNetV1.