A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling
Abstract
:1. Introduction
- Three CNN models, including ResNet50V2, were trained with a large amount of medical images. Both binary and multiclass classification systems were developed with better preprocessing and classification techniques. Hyperparameter tuning ensures the models’ efficiency, and each model offers promising results.
- The weighted average ensemble technique was implemented, where pretrained models were combined and weights were assigned according to the priority of the models. The proposed ensembled technique ensures higher accuracy than when using a single model.
- Experimental evaluations were performed and presented to support the work with proper justification. The performance of the proposed system was compared with state-of-the-art methods, and the proposed work comes up with better results and improved performance.
2. Related Work
3. Methodology
3.1. Problem Definition
3.2. Dataset Description
3.3. Deep Learning-Based Classification System
3.4. Microscopic Description of CNN Architecture
3.5. Weighted Average Ensemble
3.6. Working Functionality of the System
3.7. Proposed Algorithm
Algorithm 1: Classification with Weighted Average Ensemble | |||
Input: Chest X-ray images from the dataset | |||
K: No. of epoch | |||
M: number of models | |||
Start | |||
1. | Get Dataset | ||
2. | Extend Zipfile | ||
3. | Split Data | ||
4. | Preprocess data | ||
5. | Set m to 0 | ||
6. | Do Model Train | ||
7. | Set Model | ||
8. | Set epoch to 0 | ||
9. | While epoch < k + 1 | ||
10. | Get Preprocessed image data | ||
11. | Apply Data Augmentation | ||
12. | Pass to Model | ||
13. | Get Accuracy_score | ||
14. | Get Validation Accuracy | ||
15. | Repeat Steps | ||
16. | End while | ||
17. | Save Model | ||
18. | Load Model | ||
19. | While m < 3 | ||
20. | Set pred1, Pred2, Pred3, WEPred to 0 | ||
21. | Pass input to Models | ||
22. | Get score of Pred1, Pred2, Pred3 | ||
23. | Set w1, w2 and w3 | ||
24. | Set WEP = ([w1, w2, w3].[Pred,1 Pred2, Pred3]) | ||
25. | Get Weighted Average Ensemble Accuracy_score | ||
End |
3.8. Experimental Evaluation
4. Result and Discussion
4.1. Quantitative Analysis
4.2. Qualitative Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors | Dataset Information | Important Remarks | Classification Type | Limitations |
---|---|---|---|---|
Areej et al. [1] | 550 chest X-ray images were used that were collected from Kaggle | Binary cross entropy (BCE) loss function was used for b. classification task. | Binary classification only | Only focused on binary classification, and no proper preprocessing techniques were mentioned. Comparison of the work was also missing. |
Anooja et al. [3] | Chest X-ray dataset collected from Kaggle | Nicely compared the proposed work within the existing works, and accuracy obtained from the proposed system is also high. | Only binary classification | Fewer number instances were used to train the model, which may cause reliability issues. Multiclass classification is missing. The proposed system cannot detect other diseases except COVID-19. |
Wang. et al. [7] | Covidx dataset; 13,975 CXR images across 13,870 patients | Nicely introduced RT-PCR with its performances and drawbacks. CXR analysis was drawn with proper comparison. | Both binary and multiclass classification | Dataset was not updated; an updated dataset always ensures the reliability of the system. |
Ines et al. [10] | Dr. Jkooy’s open-source GitHub repository, COVID-19-radiography | A detailed comparison was shown, and used both CT scan and CXR images. | Both binary and multiclass classification | Data instances were much fewer. |
GJIAN et al. [13] | “http://arxiv.org/abs/2003.11597 (accessed on 11 January 2023)” | Extracted features were used as the input of SVM for classification. | Only binary classification | Data instances to train the model were much fewer, which made the reliability of the system questionable. |
Ronaldas et al. [15] | “http://axiv.org/abs/2003.13865 (accessed on 18 January 2023)” | Used 3 Max-Pooling layers, 3 convolutional layers and 2 fully connected layers | Binary classification only | Only binary classification was there. Data source authenticity is questionable. |
Gaur. et al. [17] | EfficientNetB0, VGG16, InceptionV3 | Data instances used to train the model were properly preprocessed. | Both binary and multiclass classification | Data instances were less to make the system reliable. |
Sushmithawathi et al. [19] | “https://github.com/agchung/Figure1-COVID-chestxray-dataset (accessed on 20 January 2023)” | Designed a GUI-based application that can be publicly used, developed with Google Colab GPU (Tesla K80 12GB GDDR5 VRAM). | Binary classification only | Different datasets were used separately. The ensemble technique could be used to improve the overall accuracy. |
El-Kenawy et al. [24] | Two datasets were used, where the first dataset is a COVID-19 dataset with 334 CT images. The another dataset is a non-COVID-19 dataset with 794 images | Guided whale optimization technique with AlexNet model. KNN, NN, SVM classifiers were used for feature extraction. | Binary classification | The data instances used in the work were much fewer, which can create reliability issues for the proposed system. |
Chang et al. [25] | DiCOVA challenge dataset 2021 was used with 1040 instances | ResNet50 model was used and ensemble technique was applied. | Acoustic-based COVID-19 classification | Dataset was not described properly, and a much lower number of instances were used. |
Song et al. [26] | Two public datasets and a self-made dataset (3D-COVID) were used | U-Net and U-Net++ models were trained for segmenting the lung images. | Image segmentation | More efficient learning models can be implemented based on unlabeled data, which could reduce the scarcity of labeled data. |
Sen et al. [27] | SARS-CoV-2 CT scan image database and COVID-CT database were used | Both feature extraction and feature selection techniques were developed. | Two-stage feature selection approach | Some well-established CNN models could be used to obtain better results at the initial stage. |
Chang et al. [28] | DiCOVA2021 challenge dataset was used. | Both ensemble-based prediction and uncertainty estimator-based prediction were performed. | Binary classification | A proper comparison between the state-of-the-art technique and proposed technique was missing. |
Models for Radiography Dataset | Validation Accuracy (%) | Test Accuracy (%) |
---|---|---|
ResNet50V2 (Model3) | 89.42 | 91.55 |
VGG19 (Model2) | 85.50 | 83.04 |
Xception (Mode1) | 87.48 | 87.29 |
Average Ensemble | 93.02 | |
Weighted Average Ensemble (WAE) | 94.10 |
Models for Covidx Dataset | Validation Accuracy (%) | Test Accuracy (%) |
---|---|---|
Xception (Model1) | 85.82 | 88.89 |
ResNet50 (Model3) | 89.62 | 92.54 |
VGG19 (Model2) | 87.28 | 91.68 |
VGG16 | 71.45 | 73.59 |
Inception | 81.38 | 83.51 |
Weighted Average Ensemble (WAE) | 97.25 |
Parameters | Binary Classification | Multiclass Classifiction |
---|---|---|
Val_loss | 0.2619 | 0.2583 |
Val_accuracy | 87.28 | 89.42 |
Training time | 4 h 30 min | 5 h 20 min |
Epoch | 4 | 8 |
Optimizer | Adam | Adam |
Initial learning rate | 0.001 | 0.001 |
Loss function | binary cross entropy | categorical cross entropy |
Output activation fn() | Sigmoid | softmax |
Binary Classification | Multiclass Classification | |
---|---|---|
Accuracy | 0.9725 | 0.9410 |
Precision | 0.9695 | 0.9510 |
Recall | 0.9636 | 0.9331 |
Specificity | 0.9787 | - |
F1 Score | 0.9665 | 0.9404 |
Precision | Recall | F1 Score | |
---|---|---|---|
Normal | 0.79232 | 0.92121 | 0.85231 |
Viral Pneumonia | 0.99178 | 0.91105 | 0.94260 |
COVID | 0.93418 | 0.86816 | 0.89251 |
Author | Precision | Recall | F1 Score |
---|---|---|---|
Anooja I. et al. [3] | 95 | 90 | 0.9243 |
Ines C. et al. [10] | 88.5 | 86.0 | 0.865 |
H.Nasiri et al. [11] | 92.5 | 95 | 0.912 |
Ronaldas M.J. et al. [15] | 81.57 | 80.07 | 0.8081 |
Loveleen G. et al. [17] | 88.3 | 90.0 | 0.88 |
Proposed binary classification technique with WAE | 96.95 | 96.36 | 0.9665 |
Proposed multiclass classification technique with WAE | 95.1 | 95.31 | 0.9404 |
Author | Models and Methods | Total Images | Accuracy |
---|---|---|---|
Areej et al. [1] | Transfer learning with pretrained ChexNet model | 560 | 89.8273 |
Anooja et al. [3] | AlexNet | 7240 | 93.65 |
Ines et al. [10] | VGG-19, Xception | 2022 | 90.496 |
CHANGJIAN et al. [13] | Transfer learning on ResNet | 1495 | 93 |
Ronaldas et al. [15] | CNN with Lim. Adaptive histogram equalization technique | 702 | 83.29 |
Loveleen et al. [17] | EfficientNetB0, VGG16, InceptionV3 | 5010 | 92.89 |
Sushmithawathi et al. [19] | DenseNet201, ResNet50V2, Inceptionv3 | 7240 | 91.31 |
El-Kenawy et al. [24] | Guided whale optimization technique with AlexNet, KNN, NN, SVM | 1128 | 79.0 |
Chang et al. [25] | Ensemble technique with ResNet50 | 1040 | 84.53 |
Song et al. [26] | Image segmentation with U-Net and U-Net++ models | 2922 | 95.49 |
Sen et al. [27] | CNN models, Dragonfly feature selectioin technique with SVM classifier | 2482 | 90.00 |
Chang et al. [28] | Gaussian noise-based augmentation technique and transfer learning with pretrained ResNet50 model | 1040 | 91 |
He et al. [38] | Image segmentation with evolvable adversarial network using gradient penalty | 1199 | 94 |
Proposed binary classification approach | Weighted average ensemble technique with ResNet50V2, VGG19 and Xception | 30,386 | 97.25 |
Proposed multiclass classification approach | Weighted average ensemble technique with VGG19, Xception, ResNet50V2 | 7461 | 94.10 |
Train–Test Ratio in Multiclass Approach | Accuracy with Weighted Average Ensemble | Accuracy with ResNet50v2 Model | Accuracy with VGG19 Model | Accuracy with Xception Model |
---|---|---|---|---|
90:10 | 92.94 | 90.58 | 81.04 | 84.13 |
80:20 | 94.10 | 91.55 | 83.04 | 87.29 |
70:30 | 92.65 | 90.80 | 82.98 | 85.12 |
60:40 | 90.16 | 88.85 | 82.65 | 85.03 |
50:50 | 82.01 | 81.87 | 82.02 | 80.48 |
40:60 | 71.47 | 68.23 | 63.42 | 61.53 |
Train–Test Ratio in Binary Approach | Accuracy with Weighted Average Ensemble | Accuracy with ResNet50v2 Model | Accuracy with VGG19 Model | Accuracy with Xception Model |
90:10 | 95.29 | 92.33 | 90.08 | 87.89 |
80:20 | 97.25 | 92.54 | 91.68 | 88.89 |
70:30 | 91.65 | 87.65 | 84.93 | 74.65 |
60:40 | 78.21 | 81.35 | 70.64 | 65.53 |
50:50 | 64.30 | 68.49 | 62.67 | 60.68 |
40:60 | 60.01 | 61.64 | 54.52 | 51.67 |
Experiment No. | Multiclass Classification Accuracy | Binary Classification Accuracy |
---|---|---|
Learning experiment 1 | 94.10 | 97.25 |
Learning experiment 2 | 94.09 | 97.21 |
Learning experiment 3 | 94.12 | 97.24 |
Learning experiment 4 | 94.10 | 97.25 |
Learning experiment 5 | 94.10 | 97.25 |
Learning experiment 6 | 94.08 | 97.24 |
Learning experiment 7 | 94.09 | 97.24 |
Learning experiment 8 | 94.08 | 97.24 |
Learning experiment 9 | 94.10 | 97.25 |
Learning experiment 10 | 94.10 | 97.24 |
Mean accuracy | 94.096 | 97.24 |
Standard deviation | 0.0117 | 0.0119 |
Split Count | WAE in Binary Approach | WAE in Multiclass Approach |
---|---|---|
1 | 87.24 | 90.41 |
2 | 81.67 | 81.35 |
3 | 91.74 | 88.64 |
4 | 79.61 | 84.32 |
5 | 90.25 | 80.56 |
Mean | 86.1 | 86.05 |
Standard deviation | 5.29 | 4.98 |
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Share and Cite
Chakraborty, G.S.; Batra, S.; Singh, A.; Muhammad, G.; Torres, V.Y.; Mahajan, M. A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling. Diagnostics 2023, 13, 1806. https://doi.org/10.3390/diagnostics13101806
Chakraborty GS, Batra S, Singh A, Muhammad G, Torres VY, Mahajan M. A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling. Diagnostics. 2023; 13(10):1806. https://doi.org/10.3390/diagnostics13101806
Chicago/Turabian StyleChakraborty, Gouri Shankar, Salil Batra, Aman Singh, Ghulam Muhammad, Vanessa Yelamos Torres, and Makul Mahajan. 2023. "A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling" Diagnostics 13, no. 10: 1806. https://doi.org/10.3390/diagnostics13101806
APA StyleChakraborty, G. S., Batra, S., Singh, A., Muhammad, G., Torres, V. Y., & Mahajan, M. (2023). A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling. Diagnostics, 13(10), 1806. https://doi.org/10.3390/diagnostics13101806