Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images
Abstract
:1. Introduction
- A new, well fine-tuned CNN architecture named COV-Net with fewer parameters is proposed to diagnose COVID-19 efficiently.
- Using edges exploitation operation in an optimized structure with the convolutional operator facilitates learning edges-related features of infection patterns in chest X-ray images. It leads to improved detection of COVID-19 in a timely manner.
- A D-HL-based framework for COVID-19 and pneumonia identification in chest X-ray images was proposed by using new deep CNN and SVM.
- We exploit the structural and empirical risk error minimization using the proposed COV-Net and ML classifier in hybrid learning (HL) for COVID-19 analysis. In the proposed deep hybrid learning scheme, the learning capability of the proposed CNN is explored and ML classifiers are used to enhance the discrimination proficiency of the proposed framework for chest X-ray analysis.
2. Related Work
- Most of the work presented in the past has been assessed using only accuracy, but recall, precision, and F-score are better performance measures to evaluate the generalization of the model for the complex dataset.
- In most of the previous works, only COVID-19 detection is performed. However, simply detecting COVID-19 is insufficient to diagnose other severe abnormalities, e.g., pneumonia.
- In COVID-19 analysis, the detection rate of infected X-ray images from normal individuals is still challenging because of fewer inter-class variations.
3. Methods and Materials
3.1. Dataset
3.2. Data Augmentation
3.3. Proposed CNN Architecture
3.4. Implementation Details
3.5. Initial Training
3.6. Feature Extraction Using Proposed CNN Architecture
- (a)
- As we go to the higher layer, activation started to keep fewer data.
- (b)
- At a deep level, the information became more detailed.
3.7. Classification Using Conventional ML Classifiers
3.7.1. SVM
3.7.2. k_NN
3.7.3. Naïve Bayes
3.7.4. Random Forest
3.8. Performance Metrics
3.8.1. Precision
3.8.2. Recall
3.8.3. Accuracy
3.8.4. F-Score
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Values |
---|---|
Random Rotation | [−5, 5] |
Random Horizontal Translation | [−0.5, 1] |
Random Vertical Translation | [−0.5, 1] |
Features Layer | Feature Dimension |
---|---|
FC-1 | 1 × 1 × 4096 |
Classifiers | Parameters | Type | TP | FP | FN | Recall (%) | Precision (%) | F1-Score (%) | Accuracy (%) |
---|---|---|---|---|---|---|---|---|---|
KNN | K = 2 | Cov | 60 | 0 | 0 | 100 | 100 | 100 | 100 |
Pne | 60 | 0 | 0 | ||||||
K = 3 | Cov | 60 | 0 | 0 | 100 | 100 | 100 | 100 | |
Pne | 60 | 0 | 0 | ||||||
K = 4 | Cov | 60 | 0 | 0 | 100 | 100 | 100 | 100 | |
Pne | 60 | 0 | 0 | ||||||
K = 5 | Cov | 59 | 1 | 0 | 99.2 | 99.2 | 99.2 | 99.2 | |
Pne | 60 | 0 | 1 | ||||||
SVM | Linear | Cov | 60 | 0 | 1 | 99.21 | 99.22 | 99.21 | 99.23 |
Pne | 59 | 1 | 0 | ||||||
RBF | Cov | 60 | 0 | 1 | 99.2 | 96.2 | 97.7 | 99.2 | |
Pne | 59 | 1 | 0 | ||||||
Gaussian | Cov | 60 | 0 | 1 | 99.2 | 99.2 | 99.2 | 99.2 | |
Pne | 50 | 1 | 0 | ||||||
PolyOrder-2 | Cov | 60 | 0 | 0 | 100 | 100 | 100 | 100 | |
Pne | 60 | 0 | 0 | ||||||
PolyOrder-3 | Cov | 60 | 0 | 1 | 99.2 | 99.2 | 99.2 | 99.2 | |
Pne | 59 | 1 | 0 | ||||||
PolyOrder-4 | Cov | 60 | 0 | 0 | 100 | 100 | 100 | 100 | |
Pne | 60 | 0 | 0 | ||||||
PolyOrder-5 | Cov | 60 | 0 | 0 | 100 | 100 | 100 | 100 | |
Pne | 60 | 0 | 0 | ||||||
Decision tree | Cov | 55 | 5 | 1 | 95 | 95.2 | 95.1 | 95.0 | |
Pne | 59 | 1 | 5 | ||||||
Naïve Bayes | Cov | 59 | 1 | 1 | 98.3 | 98.3 | 98.3 | 98.3 | |
Pne | 59 | 1 | 1 | ||||||
RF | max no. of splits 5 | Cov | 59 | 1 | 0 | 99.15 | 99.2 | 99.2 | 99.2 |
Pne | 60 | 0 | 1 |
Classifiers | Parameters | Type | TP | FP | FN | Recall (%) | Precision (%) | F1-Score (%) | Accuracy (%) |
---|---|---|---|---|---|---|---|---|---|
K-Nearest Neighbors | K = 2 | Cov | 56 | 4 | 5 | 92.2 | 92.2 | 92.2 | 92.2 |
Pne | 56 | 4 | 4 | ||||||
Nor | 54 | 6 | 5 | ||||||
K = 3 | Cov | 56 | 3 | 3 | 93.3 | 93.3 | 93.3 | 93.3 | |
Pne | 58 | 2 | 5 | ||||||
Nor | 54 | 6 | 3 | ||||||
K = 4 | Cov | 57 | 3 | 5 | 93.3 | 93.3 | 93.3 | 93.3 | |
Pne | 58 | 2 | 4 | ||||||
Nor | 53 | 7 | 3 | ||||||
K = 5 | Cov | 53 | 7 | 3 | 92.2 | 92.4 | 92.3 | 92.2 | |
Pne | 58 | 2 | 8 | ||||||
Nor | 55 | 5 | 3 | ||||||
Decision Tree | Cov | 50 | 10 | 11 | 81.1 | 81.1 | 81.1 | 81.1 | |
Pne | 48 | 12 | 11 | ||||||
Nor | 48 | 12 | 12 | ||||||
Naïve Bayes | Cov | 57 | 3 | 6 | 92.2 | 92.3 | 92.2 | 92.2 | |
Pne | 56 | 4 | 3 | ||||||
Nor | 53 | 7 | 5 | ||||||
Random Forest | max no. of splits 5 | Cov | 56 | 4 | 2 | 95 | 95.1 | 95.1 | 95.0 |
Pne | 60 | 0 | 5 | ||||||
Nor | 55 | 5 | 2 | ||||||
SVM | Linear | Cov | 56 | 4 | 2 | 96.6 | 96.7 | 96.7 | 96.7 |
Pne | 60 | 0 | 2 | ||||||
Nor | 58 | 2 | 2 | ||||||
Gaussian | Cov | 57 | 3 | 5 | 94.5 | 94.5 | 94.5 | 94.4 | |
Pne | 58 | 2 | 3 | ||||||
Nor | 55 | 5 | 2 | ||||||
RBF | Cov | 56 | 4 | 5 | 93.9 | 93.9 | 93.9 | 93.9 | |
Pne | 58 | 2 | 3 | ||||||
Nor | 55 | 5 | 3 | ||||||
Poly- Order3 | Cov | 57 | 3 | 3 | 95.6 | 95.6 | 95.6 | 95.6 | |
Pne | 60 | 0 | 3 | ||||||
Nor | 55 | 5 | 2 | ||||||
Poly- Order4 | Cov | 57 | 3 | 3 | 95 | 95.1 | 95.03 | 95.0 | |
Pne | 60 | 0 | 4 | ||||||
Nor | 54 | 6 | 2 | ||||||
Poly- Order5 | Cov | 56 | 4 | 3 | 94.43 | 94.5 | 94.5 | 94.4 | |
Pne | 60 | 0 | 5 | ||||||
Nor | 54 | 6 | 2 |
Model | Recall | Precision | Accuracy | F-Score |
---|---|---|---|---|
Proposed COV-Net | 95.0% | 95.07% | 95.0% | 95.03% |
Proposed D-HL-based Framework | 96.69% | 96.72% | 96.73% | 96.71% |
Author | Methodology | Recall | Precision | Accuracy | F-score |
---|---|---|---|---|---|
Apostolopoulos et al. (2020) [40] | VGG19, MobileNet, Inception, Xception, Inception ResNet v2. | 98.6% | - | 94.72% | - |
H.S Maghdid et al. (2021) [24] | Transfer learning with AlexNet model | 72% | - | 94.1% | - |
A. Narin et al. (2020) [19] | Pre-trained CNN architectures: ResNet50, ResNet101, ResNet152, inception-ResNetV2 and InceptionV3 | 91.8% | 76.5% | 96% | 83.5% |
Arora, R. et al. (2021) [45] | CNN architecture DenseNet & GoogleNet | 91% | - | - | 91% |
Makris A. et al. (2020) [43] | 5 pre-trained CNNs | - | - | 95% | - |
Proposed DH-L Framework | Proposed COV-Net with conventional ML classifier | 96.69% | 96.72% | 96.73% | 96.71% |
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Alqahtani, A.; Zahoor, M.M.; Nasrullah, R.; Fareed, A.; Cheema, A.A.; Shahrose, A.; Irfan, M.; Alqhatani, A.; Alsulami, A.A.; Zaffar, M.; et al. Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images. Life 2022, 12, 1709. https://doi.org/10.3390/life12111709
Alqahtani A, Zahoor MM, Nasrullah R, Fareed A, Cheema AA, Shahrose A, Irfan M, Alqhatani A, Alsulami AA, Zaffar M, et al. Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images. Life. 2022; 12(11):1709. https://doi.org/10.3390/life12111709
Chicago/Turabian StyleAlqahtani, Ali, Mirza Mumtaz Zahoor, Rimsha Nasrullah, Aqil Fareed, Ahmad Afzaal Cheema, Abdullah Shahrose, Muhammad Irfan, Abdulmajeed Alqhatani, Abdulaziz A. Alsulami, Maryam Zaffar, and et al. 2022. "Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images" Life 12, no. 11: 1709. https://doi.org/10.3390/life12111709
APA StyleAlqahtani, A., Zahoor, M. M., Nasrullah, R., Fareed, A., Cheema, A. A., Shahrose, A., Irfan, M., Alqhatani, A., Alsulami, A. A., Zaffar, M., & Rahman, S. (2022). Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images. Life, 12(11), 1709. https://doi.org/10.3390/life12111709