Table 1.
Recent literature on chest disease identification using the DL model.
Fig 1.
CSI, CXR, and CT scans are the three diagnostic tools that are indicated for use in the process of identifying a variety of chest disorders.
Fig 2.
Sample CXR and CT scan images of multiple chest diseases.
Table 2.
Summary of the datasets of CXR and CT scans of several chest diseases.
Table 3.
Statistics of the cough sound datasets.
Fig 3.
Scalogram image of multiple chest diseases coughs sound.
Table 4.
Summary of the datasets of CXR, CT scans, and CSI of several chest diseases after applying SMOTE Tomek.
Fig 4.
Steps of conducting pre-processing.
Table 5.
CXR, CT scan, and CSI size and storage at each preprocessing step.
Fig 5.
Summary of the proposed models.
Table 6.
Architecture of proposed base model P (1).
Table 7.
Description of four proposed models.
Table 8.
Hyperparameters value utilized for fine-tuning the proposed models.
Fig 6.
Generating CXR, CT scan, and CSI using MWDG.
(a) Rotation, (b) HST, (c) VST, (d) NI, (e) GCOR, (f) RTS, and (g) Scaling.
Fig 7.
Training-Validation accuracy and loss.
(a) Vgg-19, (b) ResNet-101, (c) ResNet-50, (d) DenseNet-121, (e) Inception-V3, (f) EfficientNetB0, (g) DenseNet-201, and (h) Proposed P(4) model.
Table 9.
Results comparison of proposed model P (1) to P (4) over validation set of 5 runs.
Table 10.
Results comparison of the proposed model with other baseline models.
Fig 8.
(a) Vgg-19, (b) ResNet-101, (c) ResNet-50, (d) DenseNet-121, (e) Inception-V3, (f) Proposed P(4) model, (g) EfficientNetB0, and (h) DenseNet-201.
Fig 9.
AU(ROC) for class wise evaluation of chest diseases.
(a) Vgg-19, (b) ResNet-101, (c) ResNet-50, (d) DenseNet-121, (e) Inception-V3, (f) Proposed P(4) model, (g) EfficientNetB0, and (h) DenseNet-201.
Fig 10.
GRAD-CAM visualization of the proposed model for highlighting the infected region of nine chest diseases.
Table 11.
Results were obtained by integrating different modules into proposed models.
Table 12.
Comparison of the proposed model with modern SOTA models.