Table 11 Machine learning and sub-fields in COVID-19

Wang et al. [168]

X-Ray

COVIDx:

13,975 – COVID-19 + 

COVIDNet:

Machine Driven Design Exploration: Projection-Expansion-Projection-Extension (PEPX)

Architecture

Accuracy—93.30

Sensitivity – 91.00

Positive Predictive Value – 98.90

Keles et al. [169]

X-Ray

210—COVID-19 + 

350—Viral Pneumonia

350—Normal

910—Total

COV19-CNNet:

Feature Engineering—7 convolutional layers

Classification—4 Dense Layer

Accuracy—94.28

Specificity—96.94

Sensitivity—94.33

F1-score—94.20

COV19-ResNet:

(Based on ResNet)

Accuracy—97.61

Specificity – 98.72

Sensitivity – 97.61

F1-score – 97.62

Ohata et al. [170]

X-Ray

Dataset-A:

194—COVID-19 + 

194 – Healthy

388—Total

Transfer Learning with MobileNet + Linear SVM

Accuracy—98.46

F1-score—98.46

FPR – 1.026

Dataset-B:

194—COVID-19 + 

194—Healthy

388—Total

Densenet201 + MLP

Accuracy—95.64

F1-score—95.63

FPR – 4.103

Singh et al. [171]

X-Ray

Dataset-A:

573—COVID-19 + 

573—Normal

573 – Pneumonia

1,719—Total

Dataset-B:

1,519—COVID-19 + 

1,519—Normal

1,519—Pneumonia

4,557—Total

Dataset-C:

573—COVID-19 + 

1,600—Normal

1,600 – Pneumonia

3,773—Total

COVIDScreen (Pruned Ensemble Learning framework):

Base Learners –

VGG-19, VGG-16, DenseNet-121, DenseNet-169, ResNet-50

Meta learner –

Naïve Bayes

 + 

GAN

Accuracy—98.67

Precision – 100.00

Recall – 100.00

F1-score – 100.00

Kappa score—0.98

Iqbal et al. [172]

X-Ray

Dataset-1:

284—COVID-19 + 

310—Normal

330—Pneumonia Bacterial

327—Pneumonia Viral

1,251—Total

Dataset-2:

157—COVID-19 + 

500—Normal,

500—Pneumonia,

1,157—Total

CoroNet:

Xception (An Extreme Version of Inception

Model – 71 Layer), Flatten, Dropout, Dense

CoroNet on Dataset-1:

Average -

Precision- 93.17

Recall—98.25

Specificity – 97.90

F1-Score—95.61

Accuracy 4 class—89.60

Accuracy 3 class – 95.00

Accuracy 2 class—99.00

CoroNet on Dataset-2:

Overall Accuracy- 90.21

Precision – 97.00

Recall – 89.00

Specificity—99.6

F-measure – 93.00

Overall 3 and 4 Class CoroNet:

Accuracy-89.60

Madaan et al. [173]

X-Ray

(Frontal Postero-

anterior)

Dataset-1:

196—COVID-19 + 

Dataset-2:

1,583—COVID-19-

XCOVNet:

Convolution (First – 32, Second – 64, Third—128)

 + ReLu + Adam Optimizer

Accuracy—98.44

Das et al. [174]

X-Ray

(Frontal)

Generated:

538—Class 0 (COVID-19 +)

468—Class 1 (COVID-19-)

1,006—Total

Ensemble method:

Combination of InceptionV3, Resnet50V2 and Densenet201

Accuracy- 91.62

Sensitivity– 95.09

Specificity—88.33

F1-score—91.71

AUC—91.71

Hussain et al. [175]

X-Ray

COVID-R:

2,843—COVID19 + 

3,108—Normal

1,439 – Pneumonia (Viral + 

Bacteria)

7,390—Total

CoroDet model(22-layer):

9 Conv2d layers, 9 maxpool2d layers, 1 Flatten, 2 dense, 1 LeakyReLu

2 class classification:

Accuracy—99.12

3 class classifications:

Accuracy—94.20

4 class classification:

Accuracy—91.20

Rahman et al. [176]

X-Ray

COVQU:

3,616—COVID19 + 

8,851—Normal

6,012 – Non-COVID

Total – 18,479 CXR

Lung segmentation:

Modified U-net

Classification:

7 Deep CNN model (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet and a shallow CNN model)

Lung segmentation:

Accuracy—98.63

Dice Score – 96.94

Classification:

Accuracy—96.29

Sensitivity- 97.28

F1-score—96.28

Narin et al. [177]

X-Ray

Dataset-1:

341—COVID-19 + 

2,800—Normal

3,141—Total

Dataset-2:

341—COVID-19 + 

1,493—Viral pneumonia

1,834- Total

Dataset-3:

341- COVID-19 + 

2,772 – Bacterial pneumonia

3,113—Total

InceptionV3, ResNet50, ResNet101, ResNet152, Inception-ResNetV2

Binary Classification:

Accuracy:

Dataset-1:

COVID-19—96.10

Dataset-2:

COVID-19—99.50

Dataset-3:

COVID-19—99.70

Gaffari Celik [178]

CT scan

&

X-Ray

CT scan images:

1,601– COVID-19 + 

1,693 – Normal

3,294 – Total

X-Ray images:

3,616 – COVID-19 + 

10,192 – Normal

6,012—Lung Opacity

1,345—Viral pneumonia

21,165 – Total

CovidDWNet:

Feature Reuse Residual Block and

Depth-wise Dilated Convolutions

 + 

Gradient Boosting Architecture

Binary Class: (CT Images)

Accuracy – 100.00 (Application 1)

Accuracy – 99.84 (Application 2)

Multi-Class: (X-Rays)

Accuracy – 96.81 (Application 3)

Multi-Class (CT and X-Rays)

Accuracy – 96.32 (Application 4)

Gozes et al. [179]

CT scan

829—COVID-19 + 

1,036—COVID-19-

1,865—Total

Lung Segmentation:

Proposed U-net with VGG-16 base encoder

Classifier:

ResNet-50

AUC – 94.80 (95% CI: 0.912–0.985)

Ahuja et al. [180]

CT scan

349—COVID19 + 

397 – NonCOVID19

746—Total

Augmentation:

Rotation + Translation + Shearing + SWT

Transfer Learning:

SqueezeNet, ResNet18, ResNet50, ResNet101

Binary Class: ResNet18

Accuracy—99.40

Sensitivity- 100.00

Specificity – 98.60

F1-score – 99.50

NPV – 100.00

Silva et al. [181]

CT scan

SARS-CoV-2 CT scan:

1,252—COVID19 + 

1,230 – NonCOVID19

2,482—Total

COVID-CT:

349—COVID19 + 

463 – NonCOVID19

812—Total

EffiecintCovidNet:

Transfer Learning -

Base Learner—EfficientNet B0 Architecture

Accuracy—98.99

Sensitivity – 98.80

Positive Prediction – 99.20