EDNC: Ensemble Deep Neural Network for COVID-19 Recognition
Abstract
:1. Introduction
- We propose EDNC (F-EDNC, FC-EDNC, and O-EDNC) ensemble deep neural network for COVID-19 recognition, which helps clinicians rapidly and accurately analyze and recognize COVID-19 lung infections from chest CT scans.
- A deep neural network named CANet has been developed and built from scratch for comparative analysis with EDNC.
- Our proposed F-EDNC has achieved an accuracy of 97.55%, followed by FC-EDNC (97.14%) and O-EDNC (96.32%).
- A web application allows users to use F-EDNC easily.
2. Materials and Methods
2.1. The Dataset
2.1.1. Main Dataset
2.1.2. Alternative Dataset
2.1.3. DICOM Format Dataset
2.2. Data Preprocessing
2.3. Modelling
2.3.1. Transfer Learning Models
- An average pooling layer, which produces a down-sampled feature map by averaging the values of all pixels in each batch of the feature map, and the calculating procedure is shown in Figure 4. The output size of the pooling layer is calculated as follows:
- A flattened layer to convert the down-sampled feature map to a one-dimensional array;
- A fully connected layer with 64 filters and a Rectified Linear Unit (ReLU) activation to connect each neuron in layers before and after. ReLU helps to solve the problem of vanishing gradients. It is calculated using the equation below.
- A dropout layer with a 0.5 dropout ratio to mitigate model overfitting problems;
2.3.2. The Proposed EDNC Architectures
F-EDNC
FC-EDNC
O-EDNC
2.3.3. CANet: A Self-Build CNN Model for Comparative Analysis
2.4. Localhost Web Application Development
2.4.1. Web Application Workflow
2.4.2. Technology Used in Building the Localhost Web Application
2.5. Model Evaluation
2.5.1. Experimental Setup
2.5.2. Confusion Matrix
2.5.3. Classification Metrics
3. Results
3.1. Results of Sixteen Modified Pre-Trained Models
3.1.1. Classification Results
3.1.2. Confusion Matrix Results
3.1.3. Learning Curve Results
3.2. Results of EDNC Models
3.2.1. Classification Results
3.2.2. Confusion Matrix Results
3.2.3. False Discovery Rate Results
3.2.4. Learning Curve Results
3.3. Classification Results of Five Runs for Pre-Trained Model and EDNC Model
3.4. Training Time and Model Size Results
3.5. Model Deployment Result
4. Discussion
- The majority of studies utilized a dataset of only a few hundred COVID-19 images, which is inadequate for developing accurate and robust deep learning methods. Insufficient data may affect the performance of proposed methods.
- In most studies, there was a data imbalance problem, with one class having more images than the other. This affects the accuracy of models.
- Additionally, there are still some other pre-trained models that have not been utilized in COVID-19 classification.
- The impact of different ensemble methods has not received adequate attention in COVID-19 research. It should be emphasized that these techniques are beneficial in both improving performances and dealing with uncertainty associated with deep learning models.
- In none of the studies was there a webpage set up for users to upload images and to obtain COVID-19 predictions.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Classes | Numbers of Samples | Format |
---|---|---|
COVID-19 | 1229 | PNG |
Non-COVID-19 | 1229 | PNG |
Classes | Numbers of Samples | Format |
---|---|---|
COVID-19 | 349 | PNG |
Non-COVID-19 | 349 | PNG |
Model | Accuracy | Precision | Sensitivity | F1 | Specificity |
---|---|---|---|---|---|
DenseNet201 | 0.9347 | 0.9388 | 0.9312 | 0.9350 | 0.9383 |
VGG16 | 0.8857 | 0.8531 | 0.9127 | 0.8819 | 0.8621 |
InceptionV3 | 0.8776 | 0.8082 | 0.9384 | 0.8684 | 0.8315 |
ResNet50 | 0.7265 | 0.7265 | 0.7265 | 0.7265 | 0.7265 |
ResNet50V2 | 0.9347 | 0.9224 | 0.9456 | 0.9339 | 0.9243 |
ResNet152V2 | 0.9265 | 0.9102 | 0.9409 | 0.9253 | 0.9170 |
Xception | 0.8898 | 0.8367 | 0.9361 | 0.8836 | 0.8524 |
VGG19 | 0.8837 | 0.9020 | 0.8701 | 0.8858 | 0.8983 |
ResNet101 | 0.7306 | 0.5429 | 0.8693 | 0.6684 | 0.6716 |
ResNet101V2 | 0.9327 | 0.9143 | 0.9492 | 0.9314 | 0.9173 |
NASNet | 0.9061 | 0.8694 | 0.9383 | 0.9025 | 0.8783 |
MobileNetV2 | 0.9265 | 0.9796 | 0.8856 | 0.9302 | 0.9772 |
MobileNet | 0.9571 | 0.9184 | 0.9956 | 0.9554 | 0.9242 |
MobileNetV3Small | 0.5000 | 0 | 0 | 0 | 0.5000 |
InceptionResNetV2 | 0.8959 | 0.8694 | 0.9181 | 0.8931 | 0.8760 |
EfficientNetB7 | 0.5000 | 1.0000 | 0.5000 | 0.6667 | 0 |
Model | Accuracy | Precision | Sensitivity | F1 | Specificity |
---|---|---|---|---|---|
ResNet50V2 | 0.9347 | 0.9224 | 0.9456 | 0.9339 | 0.9243 |
DenseNet201 | 0.9347 | 0.9388 | 0.9312 | 0.9350 | 0.9383 |
MobileNet | 0.9571 | 0.9184 | 0.9956 | 0.9554 | 0.9242 |
F-EDNC | 0.9755 | 0.9787 | 0.9641 | 0.9713 | 0.9814 |
O-EDNC | 0.9632 | 0.9551 | 0.9710 | 0.9795 | 0.9630 |
FC-EDNC | 0.9714 | 0.9837 | 0.9602 | 0.9718 | 0.9833 |
CANet | 0.9163 | 0.9061 | 0.925 | 0.9155 | 0.908 |
Model | Accuracy | Precision | Sensitivity | F1 | Specificity |
---|---|---|---|---|---|
ResNet50V2 | 0.9565 | 0.9710 | 0.9436 | 0.9571 | 0.9701 |
ResNet152V2 | 0.9510 | 0.9347 | 0.9347 | 0.9502 | 0.9673 |
MobileNet | 0.9710 | 0.9420 | 1.0000 | 0.9701 | 0.9452 |
F-EDNC | 0.9783 | 0.9565 | 1.0000 | 0.9778 | 0.9583 |
O-EDNC | 0.9710 | 0.9420 | 1.0000 | 0.9701 | 0.9452 |
FC-EDNC | 0.9783 | 0.9714 | 0.9602 | 0.9734 | 0.9602 |
CANet | 0.9237 | 0.9168 | 0.9205 | 0.9223 | 0.9115 |
Model | FDR |
---|---|
ResNet50V2 | 0.0775 |
DenseNet201 | 0.0612 |
MobileNet | 0.0816 |
F-EDNC | 0.0122 |
O-EDNC | 0.0448 |
FC-EDNC | 0.0163 |
CANet | 0.0938 |
Model | Accuracy | Precision | Sensitivity | F1 | Specificity |
---|---|---|---|---|---|
DenseNet201 | 0.9388 | 0.9610 | 0.9231 | 0.9412 | 0.9565 |
VGG16 | 0.8918 | 0.8612 | 0.8792 | 0.8701 | 0.8692 |
InceptionV3 | 0.8734 | 0.8490 | 0.8489 | 0.8491 | 0.8560 |
ResNet50 | 0.7285 | 0.7224 | 0.7314 | 0.7269 | 0.7287 |
ResNet50V2 | 0.9408 | 0.9306 | 0.9502 | 0.9403 | 0.9320 |
ResNet152V2 | 0.9224 | 0.8980 | 0.9442 | 0.9205 | 0.9027 |
Xception | 0.8939 | 0.8410 | 0.9406 | 0.8880 | 0.8571 |
VGG19 | 0.8776 | 0.8980 | 0.8627 | 0.8800 | 0.8936 |
ResNet101 | 0.7429 | 0.5673 | 0.8742 | 0.6881 | 0.6798 |
ResNet101V2 | 0.9306 | 0.9061 | 0.9527 | 0.9289 | 0.8764 |
NASNet | 0.8980 | 0.8530 | 0.9372 | 0.8931 | 0.8652 |
MobileNetV2 | 0.9020 | 0.9836 | 0.8456 | 0.9094 | 0.9805 |
MobileNet | 0.9510 | 0.9383 | 0.9661 | 0.9520 | 0.9407 |
MobileNetV3Small | 0.5000 | 0 | 0 | 0 | 0.5000 |
InceptionResNetV2 | 0.9020 | 0.8776 | 0.9227 | 0.8996 | 0.8832 |
EfficientNetB7 | 0.5000 | 1.0000 | 0.5000 | 0.6667 | 0 |
Model | Accuracy | Precision | Sensitivity | F1 | Specificity |
---|---|---|---|---|---|
ResNet50V2 | 0.9408 | 0.9306 | 0.9502 | 0.9403 | 0.9320 |
DenseNet201 | 0.9388 | 0.9610 | 0.9231 | 0.9412 | 0.9565 |
MobileNet | 0.9510 | 0.9383 | 0.9661 | 0.9520 | 0.9407 |
F-EDNC | 0.9775 | 0.9755 | 0.9795 | 0.9775 | 0.9756 |
O-EDNC | 0.9612 | 0.9592 | 0.9631 | 0.9611 | 0.9593 |
FC-EDNC | 0.9755 | 0.9836 | 0.9679 | 0.9757 | 0.9834 |
CANet | 0.9224 | 0.9347 | 0.9124 | 0.9234 | 0.9331 |
Pre-Trained Model | Accuracy | Training Time (Second/Epoch) | Parameters (MB) |
---|---|---|---|
DenseNet201 | 0.9347 | 30 | 84.37 |
VGG16 | 0.8857 | 26 | 63.08 |
InceptionV3 | 0.8776 | 26 | 90.01 |
ResNet50 | 0.7265 | 27 | 103.99 |
ResNet50V2 | 0.9347 | 26 | 103.9 |
ResNet152V2 | 0.9265 | 28 | 237.5 |
Xception | 0.8898 | 27 | 93.48 |
VGG19 | 0.8837 | 26 | 79.87 |
ResNet101 | 0.7306 | 28 | 177.19 |
ResNet101V2 | 0.9327 | 29 | 177.08 |
NASNet | 0.9061 | 32 | 25.26 |
MobileNetV2 | 0.9265 | 27 | 17.52 |
MobileNet | 0.9571 | 25 | 19.33 |
MobileNetV3Small | 0.5000 | 28 | 13.26 |
InceptionResNetV2 | 0.8959 | 30 | 213.77 |
EfficientNetB7 | 0.5000 | 34 | 263.42 |
F-EDNC (Ours) | 0.9755 | 31 | 377.2 |
O-EDNC (Ours) | 0.9632 | 31 | 337.24 |
FC-EDNC (Ours) | 0.9714 | 31 | 348 |
CANet (Ours) | 0.9163 | 26 | 338.7 |
Author | Architecture | Accuracy | F1 | Recall | Precision |
---|---|---|---|---|---|
Matsuyama, E. [17] | ResNet50 + wavelet coefficients | 92.2% | 91.5% | 90.4% | / |
Loey, M. [18] | ResNet50 + augumentation + CGAN | 82.91% | / | 77.66% | / |
Do, C. [19] | Modified DenseNet201 | 85% | / | 79% | 91% |
Polsinelli, M. [20] | Modified SqueezeNet | 85.03% | 86.20% | 87.55% | 85.01% |
Panwar, H. [45] | Modified VGG19 | 94.04 | |||
Mishra, A. [46] | Modified DenseNet121, ResNet50, VGG16, InceptionV3 and DenseNet201 | 88.3% | 86.7% | 90.15% | |
Ko, H. [21] | Modified VGG16, ResNet-50, Inception-v3, and Xception | 96.97% | |||
Maghdid, H. [22] | Modified Alexnet, A self-build CNN | 94.1% | 100% | ||
Arora, V. [47] | Modified MobileNet, DenseNet121, ResNet50, VGG16, InceptionV3 and XceptionNet | 94.12% | 96.11% | 96.11% | 96.11% |
Alshazly. H. [48] | CovidResNet and CovidDenseNet | 93.87% | 95.70 | 92.49 | 99.13% |
Yu, Z. [49] | Modified InceptionV3, ResNet50, ResNet-101, DenseNet201 | 95.34% | |||
Jaiswal, A. [50] | Modified DenseNet201 | 96.25% | 96.29% | 96.29% | 96.29% |
Sanagavarapu, S. [51] | Ensembled ResNets | 87% | 84% | 81% | 91% |
Song, J. [52] | A large-scale bi-directional generative adversarial network | 92% | |||
Sarker, L [53] | Modified Densenet121 | 96.49% | 96% | 96% | 96% |
Shan, F. [54] | VB-Net | 91.6% | |||
Wang, S. [55] | Modified DenseNet | 85% | 90% | 79% | |
Gozes, O. [56] | Modified ResNet50 | 94% | |||
Wang, S. [57] | Modified Inception | 79.3% | 63% | 83% | |
Li, L. [58] | Modified RestNet50 | 90% | |||
Proposed | EDNC | 97.75% | 97.75% | 97.95% | 97.55% |
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Yang, L.; Wang, S.-H.; Zhang, Y.-D. EDNC: Ensemble Deep Neural Network for COVID-19 Recognition. Tomography 2022, 8, 869-890. https://doi.org/10.3390/tomography8020071
Yang L, Wang S-H, Zhang Y-D. EDNC: Ensemble Deep Neural Network for COVID-19 Recognition. Tomography. 2022; 8(2):869-890. https://doi.org/10.3390/tomography8020071
Chicago/Turabian StyleYang, Lin, Shui-Hua Wang, and Yu-Dong Zhang. 2022. "EDNC: Ensemble Deep Neural Network for COVID-19 Recognition" Tomography 8, no. 2: 869-890. https://doi.org/10.3390/tomography8020071