Table 3 Classification performance comparison of different models at the image level.
From: DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images
Models | Multi-class classification | ||||
|---|---|---|---|---|---|
ACC | SEN | SPE | F1 | AUC | |
DenseNet121 | 0.8258 ± 1.88 | 0.7188 ± 1.46 | 0.8766 ± 1.98 | 0.7260 ± 1.69 | 0.7977 ± 1.60 |
ResNet50 | 0.8248 ± 2.40 | 0.7229 ± 2.81 | 0.8689 ± 2.71 | 0.7293 ± 3.06 | 0.7959 ± 2.63 |
Inceptionv3 | 0.8288 ± 0.77 | 0.7204 ± 1.20 | 0.8762 ± 1.12 | 0.7278 ± 1.31 | 0.7983 ± 1.06 |
Xception | 0.8279 ± 2.55 | 0.7189 ± 3.04 | 0.8838 ± 1.93 | 0.7294 ± 2.92 | 0.8013 ± 2.23 |
EfficientNetB4 | 0.8323 ± 1.01 | 0.7293 ± 1.23 | 0.8860 ± 1.00 | 0.7394 ± 0.92 | 0.8076 ± 0.71 |
DLA-Net (Proposed) | 0.8562 ± 1.61 | 0.7643 ± 1.44 | 0.8941 ± 1.81 | 0.7687 ± 1.53 | 0.8292 ± 1.53 |
