COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network
Abstract
:1. Introduction
2. Related Works
3. Materials and Methods
3.1. Wavelet
3.2. Multi-Resolution Analysis (MRA)
3.3. Multi-Resolution Analysis of Discrete Wavelet CNN
3.4. Proposed NeuroWavelet Capsule Network
3.5. Dataset
4. Results
4.1. Experimental Setup and Details
4.2. Discussion
4.3. Comparative Study
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Pneumonia | Data Count | Selected No | Train Set | Val Set | Test Set |
---|---|---|---|---|---|
Atelectasis | 4999 | 2980 | 1788 | 596 | 596 |
Bacteria | 3029 | 2980 | 1788 | 596 | 596 |
Cardiomegaly | 10,000 | 2980 | 1788 | 596 | 596 |
Consolidation | 10,000 | 2980 | 1788 | 596 | 596 |
COVID-19 | 3616 | 2980 | 1788 | 596 | 596 |
Effusion | 10,000 | 2980 | 1788 | 596 | 596 |
Infiltration | 10,000 | 2980 | 1788 | 596 | 596 |
Mass | 10,000 | 2980 | 1788 | 596 | 596 |
Nodule | 10,000 | 2980 | 1788 | 596 | 596 |
Pneumothorax | 10,000 | 2980 | 1788 | 596 | 596 |
Healthy | 10,000 | 2980 | 1788 | 596 | 596 |
Viral | 2983 | 2980 | 1788 | 596 | 596 |
Total | 94,627 | 35,760 | 21,456 | 7152 | 7152 |
Experiment | ACC (%) | SEN (%) | SPE (%) | PRE (%) | Time (min) |
---|---|---|---|---|---|
NW-CapsNet (MRA) | 99.6 | 99.2 | 99.1 | 99.8 | 23.2 |
NW-CapsNet (max-pooling) | 95.8 | 96.4 | 95.3 | 96.5 | 25.3 |
Model | ACC (%) | SEN (%) | SPE (%) | PRE (%) | Time (min) |
---|---|---|---|---|---|
ResNet-50 | 97.8 | 96.7 | 97.5 | 98.5 | 28.1 |
MobileNet V2 | 97.4 | 96.6 | 97.7 | 97.8 | 27.7 |
Inception V3 | 97.3 | 96.2 | 96.7 | 96.9 | 24.2 |
Inception V2 | 96.8 | 95.5 | 95.1 | 95.8 | 26.9 |
ResNet-101 | 95.3 | 93.9 | 93.7 | 94.8 | 27.3 |
DenseNet | 93.8 | 92.2 | 92.7 | 94.2 | 25.1 |
NW-CapsNet | 99.6 | 99.2 | 99.1 | 99.7 | 23.2 |
Literature | Architecture | Performance (%) |
---|---|---|
Wang et al. [9] | 2D CNN | 82.9 (ACC) |
Shi et al. [55] | Random-forest-based CNN | 87.9 (ACC), 83.3 (SEN), 90.7 (SPE) |
Chen et al. [8] | 2D Unet ++ | 95.2 (ACC), 100.0(SEN), 93.6 (SPE) |
Li et al. [45] | 2D ResNet 50 | 90.0 (SEN), 96.0 (SPE) |
Song et al. [56] | 2D ResNet 50 | 86.0 (ACC) |
Jin et al. [57] | 2D Unet++ and 2D CNN | 97.4 (SEN), 92.2 (SPE) |
Xu et al. [58] | 2D CNN | 86.7 (ACC) |
Jin et al. [59] | 2D CNN | 94.1 (SEN), 95.5 (SPE) |
Wang et al. [31] | 3D ResNet and attention | 93.3 (ACC), 87.6 (SEN), 95.5 (SPE) |
Zhang et al. [41] | 2D Unet and 2D CNN | 90.7 (SEN), 90.7 (SPE) |
NW-CapsNet | Neurowavelet and capsule network | 99.79 (ACC), 100 (SEN), 99.86 (SPE), 99.96 (AUC), 100 (PRE) |
COVID-19 Models | ACC (%) | SEN (%) | SPE (%) | PRE (%) | TIME |
---|---|---|---|---|---|
COVID-Net [31] | 97.9 | 95.9 | 96.3 | 96.9 | 24.7 |
DeCoVNet [60] | 97.8 | 96.3 | 96.4 | 96.7 | 26.1 |
Cov-Net [57] | 97.7 | 95.2 | 95.8 | 96.6 | 23.8 |
DeepPneumonia [56] | 95.1 | 95.0 | 96.2 | 95.6 | 25.1 |
NW-CapsNet | 99.6 | 99.2 | 99.1 | 99.7 | 23.2 |
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Monday, H.N.; Li, J.; Nneji, G.U.; Nahar, S.; Hossin, M.A.; Jackson, J. COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network. Healthcare 2022, 10, 422. https://doi.org/10.3390/healthcare10030422
Monday HN, Li J, Nneji GU, Nahar S, Hossin MA, Jackson J. COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network. Healthcare. 2022; 10(3):422. https://doi.org/10.3390/healthcare10030422
Chicago/Turabian StyleMonday, Happy Nkanta, Jianping Li, Grace Ugochi Nneji, Saifun Nahar, Md Altab Hossin, and Jehoiada Jackson. 2022. "COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network" Healthcare 10, no. 3: 422. https://doi.org/10.3390/healthcare10030422