Table 9 Machine learning and sub-fields in pneumonia diagnosis

Szepesi et al. [140]

X-Ray

4,273 – Pneumonia

1,583 – Normal

5,856 – Total Labeled Images

CNN

 + 

Modified Dropout

Accuracy—97.2

Recall – 97.30

Precision – 97.40

F1 Score – 97.40

AUC – 0.982

Avola et al. [141]

X-Ray

2,780 – Bacterial Pneumonia

1,493 – Viral Pneumonia

474 – COVID-19

1,583 – Normal

6,330 – Total

AlexNet, MnasNet, MobileNetv2, MobileNet v3, DenseNet, GoogleNet, ResNet50, ResNeXt, SqueezeNet, Wide ResNet50, VGG16, and ShuffleNet

Average F1 Score – 84.46

Liu et al. [142]

X-Ray

Dataset 1:

2,777 – Bacterial Pneumonia

2,838 – Viral Pneumonia

3,674 – COVID-19

11,768 – Normal

21,057 – Total

Dataset 2:

2,777 – Bacterial Pneumonia

2,838 – Viral Pneumonia

3,665 – COVID-19

3,251 – Normal

12,531 – Total

Multi-Branch Fusion Auxiliary Learning (MBFAL):

Auxiliary Learning method, and Prior-Attention Residual Learning (PARL) Architecture

MBFAL Average:

Accuracy – 95.61

Srivastava et al. [143]

X-Ray

1,656—Viral Pneumonia

1,281—COVID-19

3,270—Normal

6,207 – Total

Ensemble Model:

Ensemble DNN classifiers’ score based on Condorcet’s Jury Theorem (CJT)

And

Domain Extended Transfer Learning (DETL)

CJT -

Accuracy – 98.22

Sensitivity – 98.37

Specificity – 99.79

DETL -

Accuracy – 97.26

Sensitivity – 98.37

Specificity – 100

Qu et al. [144]

Infrared Thermal Images

 + 

RGB images

Number of Subjects:

30—Normal

28 – Pneumonia

58—Total

SVM

KNN

Decision Tree

Gaussian Naïve Bayes classifier

LDA, QDA

Binary Classification:

Accuracy – 93.00

Singh et al. [145]

X-Ray

1,345—Viral Pneumonia

371—COVID-19

1,341—Normal

3,057—Total

Hybrid Social Group Optimization algorithm + 

Support Vector Classifier

Accuracy—99.65

Chowdhury et al. [146]

X-Ray

423—COVID-19 Pneumonia

1,485—Viral Pneumonia

1,579 – Normal

3,487—Total

Three Shallow Networks:

MobileNetv2, SqueezeNet, and ResNet18

Five Deep Networks:

Inceptionv3, ResNet101, CheXNet, VGG19, and DenseNet201

Binary Classification (Normal, Pneumonia) -

Accuracy—99.70

Sensitivity – 99.70

Precision – 99.70

Specificity – 99.55

Multi Classification –

Accuracy—97.90

Sensitivity – 97.95

Precision – 97.90

Specificity – 98.80

Wong et al. [147]

CT Scan (2D/3D)

4,017—Viral Pneumonia

7,766—Bacterial Pneumonia

3,443—Mycoplasma Pneumonia

10,687—COVID-19

11,666 – Normal

37,579—Total

CNN:

Multi-Scale Attention Network (MSANet)

Accuracy—97.46

Recall – 96.18

Precision – 97.31

F1 Score – 96.71

Macro-Average AUC—0.9981

Ukwuoma et al. [148]

X-Ray

Binary Classification

(Mendeley Dataset) –

4,290—Viral Pneumonia

3,834 – Normal

8,124 – Total

Multi Classification

(Chest X-ray Dataset) -

5,000—Viral Pneumonia

5,000—Bacterial Pneumonia

5,000 – Normal

15,000—Total

Ensembled CNN

 + 

Transformer Encoder Method

Ensemble A

(DenseNet201, VGG16, GoogleNet)

Ensemble B

(DenseNet201, InceptionResNetV2, Xception)

Binary Classification (Normal, Pneumonia) -

Accuracy – 99.21

F1 Score – 99.21

Multi Classification

Accuracy – 98.19

F1 Score – 97.29

Ensemble Binary Class

Ensemble A -

Accuracy – 97.22

F1 Score – 97.14

Ensemble B -

Accuracy – 96.44

F1 Score – 96.44

Ensemble Multi-Class

Ensemble A -

Accuracy – 97.20

F1 Score – 95.80

Ensemble B -

Accuracy – 96.40

F1 Score – 94.90

Kusk et al. [149]

X-Ray

4,273—Viral and Bacterial

Pneumonia

1,583 – Normal

5,856 – Total

CNN

 + 

Gaussian noise

(Five Gaussian Noise Levels)

Accuracy – (96.80—97.60)

Sensitivity – (96.90—98.20)

Specificity – (94.40—98.70)

Li & Li [150]

X-Ray

2,530 – Bacterial Pneumonia

1,345 – Viral Pneumonia

797 – COVID-19

5,510—Healthy

10,182 – Total

17 CNNs (AlexNet, GoogleNet, Vgg16, ResNet18, SqueezeNet, MobileNetv2, Inceptionv3, DenseNet201, Xception, Vgg19, Places365GoogleNet, InceptionResNetv2, ResNet50, ResNet101, NASNetMobile, NASNetLarge, ShuffleNet)

Distinguishing Covid-19 Pneumonia from Bacterial

Pneumonia -

(Accuracy – 99.85)

Normal Lung Images (Accuracy – 100)

Viral Covid-19 Pneumonia

(Accuracy – 99.95)

Bhandari et al. [151]

X-Ray

4, 273 – Pneumonia

576—COVID-19

700 – TB

1583 – Normal

7,132 – Total

CNN

 + 

XAI

 + 

Grad-CAM, Local Interpretable

Modelagnostic Explanation (LIME), and SHapley Additive exPlanation (SHAP)

Overall Accuracy – 95.94

Average -

Specificity – 95.71 ± 1.55

Sensitivity – 95.50 ± 1.72

F1 Score – 96.53 ± 0.95

Khaniabadi et al. [152]

CT Scan

100 – Pneumonia

100 – COVID-19

100—Healthy

300 – Total

ML Algorithms:

SVM, KNN, Decision Tree, Naïve Bays, Bagging, Random Forest, and Ensemble Meta voting

Random Forest, and Ensemble Meta voting -

Accuracy(RF) – 0.94 ± 0.031

Accuracy(EM) – 0.92 ± 0.034

Sensitivity(RF) – 0.90 ± 0.056

Sensitivity(EM)—0.90 ± 0.078

Specificity(RF) – 0.95 ± 0.020

Specificity(RF) – 0.95 ± 0.010

AUC—0.98 ± 0.010

AUC—0.92 ± 0.043

Ascencio-Cabral et al. [153]

CT Scan

2,946—Community Acquired

Pneumonia

7,593 – COVID-19

6,893 – Non-COVID-19

17,432 – Total

Transfer Learning:

ResNet-50, ResNet-50r, DenseNet-121, MobileNet-v3, and CaiT-24-XXS-224 (CaiT) Transformer

ResNet-50’s –

Accuracy – 98.00

Balanced Accuracy – 98.00

F1 Score – 98.00

F2 Score – 98.00

MCC – 98.00

Sensitivity – 98.00

Specificity – 98.00