A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images
Abstract
:1. Introduction
- The proposed deep-learning model consisted of various efficient modules for COVID-19 classification. The inception residual deep-learning model inspires them. We presented different inception residual blocks that cater to information using different depths feature maps at different scales with other layers. The features are concatenated at each proposed classification block, using average pooling and concatenated features to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset.
- The multiscale features are extracted at different levels of the proposed DL model and embed these features to different ML algorithms to validate the combination of DL and ML models.
- The RISE-based python-based library was used to visualize the activation of feature maps, and also, the SHAP library was used to check the importance of features extracted from the deep-learning model. The visualization results showed the convergence region receiver operating characteristics (ROC), and precision–recall curves showed our proposed technique performance and validation.
2. Method
2.1. Data Collection
2.2. Development of System Architecture: For Discussion
2.2.1. Contribution Using First Approach
2.2.2. Contribution Using the Second Approach
- The objective of the second approach is to combine deep-learning and machine-learning models.
- The features are extracted from the proposed deep-learning model and passed features to the traditional machine-learning model for classification of COVID-19.
- The multiscale features are extracted from various blocks from proposed Inception-ResNet blocks are concatenated, and after the concatenation of these features are used in machine-learning classifiers.
2.2.3. Approach 1: Proposed CoVIRNet Deep Learning
2.2.4. Approach 2: Deep-Feature-Extraction and Machine-Learning Models
Random Forest
Bagging Tree
Gradient Boosting Classifier
Perceptron Model Multilayer
Logistic Regression
3. Results and Discussions
3.1. Data Visualization
3.2. Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sr. No. | Disease | Total Samples | Training Samples | Testing Samples |
---|---|---|---|---|
1 | Normal images | 310 | 248 | 62 |
2 | Pneumonia-bacterial-infection images | 330 | 264 | 66 |
3 | Viral-pneumonia images | 327 | 261 | 66 |
4 | Corona-infection images | 284 | 227 | 57 |
Model | Accuracy | Precision | Recall | F1score |
---|---|---|---|---|
Xception—Fine-Tuned | 0.8822 | 0.88041 | 0.8796 | 0.8787 |
CoVIRNet Model | 0.9578 | 0.9491 | 0.9544 | 0.9509 |
ResNet101—Fine-Tuned | 0.8880 | 0.8954 | 0.9033 | 0.8923 |
MobielNetV2—Fine-Tuned | 0.90347 | 0.9028 | 0.9011 | 0.9005 |
DenseNet201—Fine-Tuned | 0.9419 | 0.9462 | 0.9514 | 0.9474 |
CoVIRNet with RF | 0.9729 | 0.9774 | 0.9702 | 0.9732 |
Model | Accuracy | Precision | Recall | F1score |
---|---|---|---|---|
CoVIRNet Model with LR | 0.9279 | 0.9283 | 0.9264 | 0.9263 |
CoVIRNet Model with MLP | 0.9446 | 0.9439 | 0.9437 | 0.9435 |
CoVIRNet Model with GB | 0.9523 | 0.95141 | 0.9515 | 0.9512 |
CoVIRNet Model with BT | 0.9613 | 0.9607 | 0.9607 | 0.9605 |
CoVIRNet Model with RF | 0.9729 | 0.9774 | 0.9702 | 0.9732 |
Study | Dataset | Model Used | Classification Accuracy |
---|---|---|---|
Narin et al. [43] | 2-class: 50 COVID-19/50 normal | Transfer learning with ResNet50 and Inception-v3 | 98% |
Panwar et al. [44] | 2-class: 142 COVID-19/ 142 normal | nCOVnet CNN | 88% |
Altan et al. [45] | 3-class: 219 COVID-19 1341 norma l1345 pneumonia viral | 2D curvelet transform, chaotic salp swarm algorithm (CSSA), EfficientNet-B0 | 99% |
Chowdhury et al. [46] | 3-class: 423 COVID-19 1579 normal 1485 pneumonia viral | Transfer learning with CheXNet | 97.7% |
Wang and Wong [47] | 3-class: 358 COVID-19/5538 normal/8066 pneumonia | COVID-Net | 93.3% |
Kumar et al. [48] | 3-class: 62 COVID-19/1341 normal/1345 pneumonia | ResNet1523 features and XGBoost classifier | 90% |
Sethy and Behera [49] | 3-class: 127 COVID-19/127 normal/127 pneumonia | ResNet50 features and SVM | 95.33% |
Ozturk et al. [50] | 3-class: 125 COVID-19/500 normal 500 pneumonia | DarkCovidNet CNN | 87.2% |
Khan et al. [51] | 4-class: 284 COVID-19/310 normal/330 pneumonia bacterial/327 pneumonia viral | CoroNet CNN | 89.6% |
Tanvir Mahmud et al. [52] | 4-class: 305 COVID-19 + 305 Normal + 305 Viral Pneumonia + 305 Bacterial Pneumonia | StackedMulti-resolutionCovXNet | 90.3% |
Proposed CoVIRNet DL model | 4-class: 284 COVID-19/310 normal/330 pneumonia bacterial/327 pneumonia viral | Multiscale features CoVIRNet | 95.78% |
Proposed CoVIRNet DL model with RF | 4-class: 284 COVID-19/310 normal/330 pneumonia bacterial/327 pneumonia viral | Multiscale features CoVIRNet+ RF | 97.29% |
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Almalki, Y.E.; Qayyum, A.; Irfan, M.; Haider, N.; Glowacz, A.; Alshehri, F.M.; Alduraibi, S.K.; Alshamrani, K.; Alkhalik Basha, M.A.; Alduraibi, A.; et al. A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images. Healthcare 2021, 9, 522. https://doi.org/10.3390/healthcare9050522
Almalki YE, Qayyum A, Irfan M, Haider N, Glowacz A, Alshehri FM, Alduraibi SK, Alshamrani K, Alkhalik Basha MA, Alduraibi A, et al. A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images. Healthcare. 2021; 9(5):522. https://doi.org/10.3390/healthcare9050522
Chicago/Turabian StyleAlmalki, Yassir Edrees, Abdul Qayyum, Muhammad Irfan, Noman Haider, Adam Glowacz, Fahad Mohammed Alshehri, Sharifa K. Alduraibi, Khalaf Alshamrani, Mohammad Abd Alkhalik Basha, Alaa Alduraibi, and et al. 2021. "A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images" Healthcare 9, no. 5: 522. https://doi.org/10.3390/healthcare9050522