СКАЧАТЬ
-
|
Tuberculosis
|
[26]
|
FHRNet
|
ChestX-ray14 dataset
|
-
|
50
|
Sigmoid
|
-
|
14 chest pathologies
|
[20]
|
AG-CNN
|
ChestX-ray14
|
|
50
|
Sigmoid
|
-
|
14 chest pathologies
|
[53]
|
preact-ResNet [39_chp7_14]
|
ChestX-ray14
|
-
|
-
|
Sigmoid
|
-
|
8 chest pathologies out of 14
|
[70]
|
Unified DCNN
|
ChestX-ray8
|
-
|
-
|
Sigmoid
|
-
|
8 chest pathologies out of 14
|
[28]
|
Ensemble of AlexNet, VGG-16, VGG-19, ResNet-50, ResNet-101 and ResNet-152
|
Indiana dataset
|
-
|
-
|
-
|
-
|
Cardiomegaly, Edema, and Tuberculosis
|
[5]
|
ChestNet CNN
|
ChestX-ray14
|
14
|
30
|
|
|
Consolidation
|
[49]
|
CheXNeXt CNN
|
ChestX-ray14
|
|
|
|
|
14 chest pathologies
|
[46]
|
Customized CNN
|
NIH Tuberculosis Chest X-ray, Belarus Tuberculosis
|
23
|
|
ReLU
|
|
Tuberculosis
|
[61]
|
Ensemble of RetinaNet and Mask R-CNN
|
Kaggle dataset RSNA
|
-
|
-
|
ReLU
|
-
|
Pneumonia
|
[45]
|
DarkCovid-Net
|
COVID Chest x-ray Kaggle
|
17
|
|
ReLU
|
-
|
COVID, Normal and Pneumonia
|
[65]
|
GoogLeNet
|
St. Michael’s Hospital chest x-ray
|
-
|
60
|
ReLU
|
-
|
5 pathologies: Cardiomegaly, Edema, Pleural effusion, pneumothorax, and consolidation
|
[35]
|
Ensemble of AlexNet and GoogleNet
|
NIH Tuberculosis Chest X-ray, Belarus Tuberculosis
|
-
|
-
|
-
|
-
|
Tuberculosis
|
[31]
|
ResNet101
|
Cohen and Kaggle [3]
|
-
|
-
|
-
|
-
|
COVID-19–induced pneumonia
|
Table 2.3 Comparison of models on the basis of AUC score for 14 chest pathologies.
Ref.
|
Atel
|
Card
|
Effu
|
Infi
|
Mass
|
Nodu
|
Pne1
|
Pnet
|
Cons
|
Edem
|
Emph
|
Fibr
|
PT
|
Hern
|
[10]
|
0.784
|
0.888
|
0.831
|
0.705
|
0.838
|
0.796
|
0.727
|
0.876
|
0.746
|
0.852
|
0.942
|
0.837
|
0.796
|
0.912
|
[23]
|
0.795
|
0.887
|
0.875
|
0.703
|
0.835
|
0.716
|
0.742
|
0.863
|
0.786
|
0.892
|
0.875
|
0.756
|
0.774
|
0.836
|
[70]
|
0.716
|
0.807
|
0.784
|
0.609
|
0.706
|
0.671
|
0.633
|
0.806
|
0.708
|
0.835
СКАЧАТЬ
|