Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images
Abstract
:1. Introduction
Related Works
2. Materials and Methods
2.1. Network Models
- ResNet:
- InceptionNet:
- InceptionResNetV2:
- DenseNet:
2.2. Interpretability Techniques
- Occlusion:
- Saliency:
- Input X Gradient:
- Guided Backpropagation:
- Integrated Gradients:
- DeepLIFT:
- Neuron Activation Profiles:
2.3. Implementation
2.4. Data
2.4.1. Data Collection
2.4.2. Dataset Preparation
2.4.3. Pre-Processing
2.4.4. Classification Setup
2.5. Evaluation Metrics
3. Results
3.1. Model Outcome
3.1.1. Overall Comparisons of the Classifiers
3.1.2. Comparisons of the Classifiers for Different Pathologies
3.2. Interpretability of Models
3.2.1. Pathology-Based Comparisons of Local Interpretability Techniques for Models
3.2.2. Intense Interpretability
- The failure case of the best performing model for COVID-19 classification:
- Representations in DenseNet161 and ResNet18:
- COVID-19, pneumonia and viral pneumonia:
4. Discussion
5. Conclusions and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model | No of Parameters | GFLOPs | MACs ( ) | GPU Memory (Forward + Backward) in GB |
---|---|---|---|---|
ResNet18 | 11,183,694 | 18.95 | 9.53 | 0.15 |
ResNet34 | 21,291,854 | 38.28 | 19.22 | 0.22 |
InceptionV3 | 24,382,716 | 35.04 | 17.63 | 0.44 |
DenseNet161 | 26,502,926 | 80.73 | 40.98 | 1.31 |
InceptionResNetV2 | 54,327,982 | 81.07 | 40.70 | 0.72 |
Model | Precision | Recall | F1 |
---|---|---|---|
DenseNet161 | 0.864 ± 0.012 | 0.845 ± 0.015 | 0.854 ± 0.008 |
InceptionResNetV2 | 0.844 ± 0.023 | 0.787 ± 0.063 | 0.814 ± 0.042 |
InceptionV3 | 0.802 ± 0.065 | 0.792 ± 0.044 | 0.796 ± 0.053 |
ResNet18 | 0.824 ± 0.014 | 0.824 ± 0.008 | 0.824 ± 0.007 |
ResNet34 | 0.815 ± 0.022 | 0.800 ± 0.025 | 0.807 ± 0.018 |
Ensemble | 0.889 ± 0.010 | 0.851 ± 0.005 | 0.869 ± 0.007 |
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Chatterjee, S.; Saad, F.; Sarasaen, C.; Ghosh, S.; Krug, V.; Khatun, R.; Mishra, R.; Desai, N.; Radeva, P.; Rose, G.; et al. Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images. J. Imaging 2024, 10, 45. https://doi.org/10.3390/jimaging10020045
Chatterjee S, Saad F, Sarasaen C, Ghosh S, Krug V, Khatun R, Mishra R, Desai N, Radeva P, Rose G, et al. Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images. Journal of Imaging. 2024; 10(2):45. https://doi.org/10.3390/jimaging10020045
Chicago/Turabian StyleChatterjee, Soumick, Fatima Saad, Chompunuch Sarasaen, Suhita Ghosh, Valerie Krug, Rupali Khatun, Rahul Mishra, Nirja Desai, Petia Radeva, Georg Rose, and et al. 2024. "Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images" Journal of Imaging 10, no. 2: 45. https://doi.org/10.3390/jimaging10020045
APA StyleChatterjee, S., Saad, F., Sarasaen, C., Ghosh, S., Krug, V., Khatun, R., Mishra, R., Desai, N., Radeva, P., Rose, G., Stober, S., Speck, O., & Nürnberger, A. (2024). Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images. Journal of Imaging, 10(2), 45. https://doi.org/10.3390/jimaging10020045