Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning
Abstract
:1. Introduction
Limitations of Related Work and Our Contributions
- Two-class and multi-class classification with different combinations of chest-related diseases, including COVID-19, normal, viral pneumonia, bacterial pneumonia, tuberculosis, and pneumothorax, are proposed using different pretrained CNN-based models.
- Two balanced datasets were used to classify five chest-related diseases.
- Different image augmentation techniques were used to balance or increase the dataset size.
- The proposed model was compared with state-of-the-art methods and performed well.
2. Materials and Methods
2.1. Datasets
2.2. Data Augmentation
2.3. Transfer Learning (TL) Using CNNs
2.4. Proposed Model
3. Experiment Setup and Performance Measures
4. Results and Discussion
4.1. Two-Class Classifier
4.2. Three-Class Classifier
4.3. Four-Class Classifier
4.4. Five-Class Classifier
4.5. Six-Class Classifier
5. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
- Rothan, H.A.; Byrareddy, S.N. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun. 2020, 109, 102433. [Google Scholar] [CrossRef] [PubMed]
- WHO-Director-General’s. WHO Director-General‘s Opening Remarks at the Media Briefing on COVID-19—11 March 2020. 2020. Available online: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19 (accessed on 11 March 2020).
- Ghaderzadeh, M.; Asadi, F. Deep learning in the detection and diagnosis of COVID-19 using radiology modalities: A systematic review. J. Healthc. Eng. 2021, 2021, 6677314. [Google Scholar] [PubMed]
- Chowdhury, M.E.H.; Rahman, T.; Khandakar, A.; Mazhar, R.; Kadir, M.A.; Bin Mahbub, Z.; Islam, K.R.; Khan, M.S.; Iqbal, A.; Al Emadi, N.; et al. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 2020, 8, 132665–132676. [Google Scholar] [CrossRef]
- Ker, J.; Wang, L.; Rao, J.; Lim, T. Deep learning applications in medical image analysis. IEEE Access 2017, 6, 9375–9389. [Google Scholar] [CrossRef]
- Mustafa, M.; Alshare, M.; Bhargava, D.; Neware, R.; Singh, B.; Ngulube, P. Perceived security risk based on moderating factors for blockchain technology applications in cloud storage to achieve secure healthcare systems. Comput. Math. Methods Med. 2022, 2022, 6112815. [Google Scholar] [CrossRef]
- Alzubaidi, L.; Fadhel, M.A.; Oleiwi, S.R.; Al-Shamma, O.; Zhang, J. DFU_QUTNet: Diabetic foot ulcer classification using novel deep convolutional neural network. Multimedia Tools Appl. 2020, 79, 15655–15677. [Google Scholar] [CrossRef]
- Chen, H.; Ni, D.; Qin, J.; Li, S.; Yang, X.; Wang, T.; Heng, P.A. Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J. Biomed. Health Inform. 2015, 19, 1627–1636. [Google Scholar] [CrossRef]
- Norouzifard, M.; Nemati, A.; GholamHosseini, H.; Klette, R.; Nouri-Mahdavi, K.; Yousefi, S. Automated glaucoma diagnosis using deep and transfer learning: Proposal of a system for clinical testing. In Proceedings of the 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, New Zealand, 19–21 November 2018. [Google Scholar]
- Pathak, Y.; Shukla, P.; Tiwari, A.; Stalin, S.; Singh, S. Deep transfer learning based classification model for COVID-19 disease. IRBM 2020, 43, 87–92. [Google Scholar] [CrossRef]
- Ardakani, A.A.; Kanafi, A.R.; Acharya, U.R.; Khadem, N.; Mohammadi, A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput. Biol. Med. 2020, 121, 103795. [Google Scholar] [CrossRef]
- Hemdan, E.E.-D.; Shouman, M.A.; Karar, M.E. Covidx-net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv 2020, arXiv:2003.11055. [Google Scholar]
- Hammoudi, K.; Benhabiles, H.; Melkemi, M.; Dornaika, F.; Arganda-Carreras, I.; Collard, D.; Scherpereel, A. Deep learning on chest X-ray images to detect and evaluate pneumonia cases at the era of COVID-19. J. Med Syst. 2021, 45, 75. [Google Scholar] [CrossRef] [PubMed]
- Narin, A.; Kaya, C.; Pamuk, Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal. Appl. 2021, 24, 1207–1220. [Google Scholar] [CrossRef] [PubMed]
- Loey, M.; Smarandache, F.; Khalifa, N.E.M. Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning. Symmetry 2020, 12, 651. [Google Scholar] [CrossRef] [Green Version]
- Luz, E.; Silva, P.L.; Silva, R.; Silva, L.; Guimarães, J.; Miozzo, G.; Moreira, G.; Menotti, D. Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images. Res. Biomed. Eng. 2022, 38, 149–162. [Google Scholar] [CrossRef]
- Progga, N.I.; Hossain, M.S.; Andersson, K. A deep transfer learning approach to diagnose COVID-19 using X-ray images. In Proceedings of the 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), Bhubaneswar, India, 26–27 December 2020. [Google Scholar]
- Al-Timemy, A.H.; Khushaba, R.N.; Mosa, Z.M.; Escudero, J. An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-ray Images in Resource Limited Settings, in Artificial Intelligence for COVID-19; Springer: Berlin/Heidelberg, Germany, 2021; pp. 77–100. [Google Scholar]
- Apostolopoulos, I.D.; Mpesiana, T.A. COVID-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 2020, 43, 635–640. [Google Scholar] [CrossRef] [Green Version]
- Ozturk, T.; Talo, M.; Yildirim, E.A.; Baloglu, U.B.; Yildirim, O.; Acharya, U.R. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 2020, 121, 103792. [Google Scholar] [CrossRef]
- Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 2015, 115, 211–252. [Google Scholar] [CrossRef] [Green Version]
- Asif, S.; Wenhui, Y.; Jin, H.; Jinhai, S. Classification of COVID-19 from chest X-ray images using deep convolutional neural network. In Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, China, 11–14 December 2020. [Google Scholar]
- Panwar, H.; Gupta, P.; Siddiqui, M.K.; Morales-Menendez, R.; Singh, V. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos Solitons Fractals 2020, 138, 109944. [Google Scholar] [CrossRef]
- Khan, A.I.; Shah, J.L.; Bhat, M.M. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput. Methods Programs Biomed. 2020, 196, 105581. [Google Scholar] [CrossRef]
- Das, N.N.; Kumar, N.; Kaur, M.; Kumar, V.; Singh, D. Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays. IRBM 2020, 43, 114–119. [Google Scholar]
- Wang, L.; Lin, Z.Q.; Wong, A. COVID-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci. Rep. 2020, 10, 19549. [Google Scholar] [CrossRef] [PubMed]
- Ismael, A.M.; Şengür, A. Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst. Appl. 2021, 164, 114054. [Google Scholar] [CrossRef] [PubMed]
- Vaid, S.; Kalantar, R.; Bhandari, M. Deep learning COVID-19 detection bias: Accuracy through artificial intelligence. Int. Orthop. 2020, 44, 1539–1542. [Google Scholar] [CrossRef] [PubMed]
- Farooq, M.; Hafeez, A. Covid-resnet: A deep learning framework for screening of COVID-19 from radiographs. arXiv 2020, arXiv:2003.14395. [Google Scholar]
- Masko, D.; Hensman, P. The Impact of Imbalanced Training Data for Convolutional Neural Networks. Degree Project in Computer Science, Stockholm, Sweden, 2015. Available online: https://www.kth.se/social/files/588617ebf2765401cfcc478c/PHensmanDMasko_dkand15.pdf (accessed on 25 July 2022).
- He, H.; Garcia, E.A. Learning from Imbalanced Data. IEEE Trans. Knowl. Data Eng. 2009, 21, 1263–1284. [Google Scholar]
- Chen, C.; Liaw, A.; Breiman, L. Using random forest to learn imbalanced data. Univ. Calif. Berkeley 2004, 110, 24. [Google Scholar]
- Chan, Y.-H.; Zeng, Y.-Z.; Wu, H.-C.; Wu, M.-C.; Sun, H.-M. Effective pneumothorax detection for chest X-ray images using local binary pattern and support vector machine. J. Healthc. Eng. 2018, 2018, 2908517. [Google Scholar] [CrossRef] [Green Version]
- Kermany, D.S.; Goldbaum, M.; Cai, W.; Valentim, C.C.S.; Liang, H.; Baxter, S.L.; McKeown, A.; Yang, G.; Wu, X.; Yan, F.; et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018, 172, 1122–1131.e9. [Google Scholar] [CrossRef]
- Jaeger, S.; Karargyris, A.; Candemir, S.; Folio, L.; Siegelman, J.; Callaghan, F.; Xue, Z.; Palaniappan, K.; Singh, R.K.; Antani, S.; et al. Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med Imaging 2013, 33, 233–245. [Google Scholar] [CrossRef]
- Candemir, S.; Jaeger, S.; Palaniappan, K.; Musco, J.P.; Singh, R.K.; Xue, Z.; Karargyris, A.; Antani, S.; Thoma, G.; McDonald, C.J. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med Imaging 2013, 33, 577–590. [Google Scholar] [CrossRef]
- Issarti, I.; Consejo, A.; Jiménez-García, M.; Hershko, S.; Koppen, C.; Rozema, J. Computer aided diagnosis for suspect keratoconus detection. Comput. Biol. Med. 2019, 109, 33–42. [Google Scholar] [CrossRef] [PubMed]
- Yan, Y.; Chen, M.; Shyu, M.-L.; Chen, S.-C. Deep learning for imbalanced multimedia data classification. In Proceedings of the 2015 IEEE International Symposium on Multimedia (ISM), Miami, FL, USA, 14–16 December 2015. [Google Scholar]
- SIIM. SIIM-ACR Pneumothorax Segmentation. Available online: https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation (accessed on 25 July 2022).
- Sethy, P.K.; Behera, S.K. Detection of coronavirus disease (COVID-19) based on deep features. Int. J. Math. Eng. Manag. Sci. 2020, 5, 643–651. [Google Scholar]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Commun. ACM 2017, 60, 84–90. [Google Scholar] [CrossRef] [Green Version]
- Patterson, J. CNN Architecture Overview. 2017. Available online: https://www.oreilly.com/library/view/deep-learning/9781491924570/ (accessed on 3 July 2022).
- Johnson, J.M.; Khoshgoftaar, T.M. Survey on deep learning with class imbalance. J. Big Data 2019, 6, 1–54. [Google Scholar] [CrossRef] [Green Version]
- Jangam, E.; Annavarapu, C.S.R. A stacked ensemble for the detection of COVID-19 with high recall and accuracy. Comput. Biol. Med. 2021, 135, 104608. [Google Scholar] [CrossRef]
- Haritha, D.; Pranathi, M.K.; Reethika, M. COVID detection from chest X-rays with DeepLearning: CheXNet. In Proceedings of the 2020 5th International Conference on Computing, Communication and Security (ICCCS), Patna, India, 14–16 October 2020. [Google Scholar]
Publications | Datasets | No of Classes | Train/Test Ratio | Models | Accuracy | Characteristics |
---|---|---|---|---|---|---|
Chowdhury et al. [4] | COVID-19 = 423 Viral pneumonia = 1485 Normal = 1579 | 2 __________ 3 | 80/20 | MobileNetv_2, SqueezeNet, ResNet18, Inceptionv_3, ResNet101, Chex Net, VGG19, DenseNet201 | 99.7% ________ 97.95% | Unbalanced classes |
Ozturk et al. [20] | COVID-19 = 125 Pneumonia = 500 Normal = 500 | 2 __________ 3 | 80/20 | DarkCovidNet | 98.08% ________ 87.02% | Unbalanced classes Low accuracy |
Panwar et al. [23] | COVID-19 = 142 Normal 142 | 2 | 70/30 | nCovNet | 88% | Balanced classes with limited dataLow accuracy |
Khan et al. [24] | COVID-19 = 284 Viral pneumonia = 327 Bacterial pneumonia = 330 Normal = 310 | 2 __________ 3 __________ 4 | - | CoroNet | 99% ________ 95% ________ 89.6% | Unbalanced classes Low accuracy |
Wang et al. [26] | COVID-19 = 358 Pneumonia = 5538 Normal = 8066 | 3 | - | COVID-net | 93.3% | Unbalanced classes |
Narin et al. [14] | COVID-19 = 341 Normal = 2800 | 2 | 80/20 | ResNet101, ResNet_v2, Inception_v3, ResNet50 | 96.1% | Unbalanced classes |
Ismael et al. [27] | COVID-19 = 180, Normal = 200 | 2 | 75/25 | VGG16, ResNet50, ResNet18, ResNet101, VGG19 | 94.7% | Unbalanced classes Low accuracy |
Disease | No. of Images for Dataset-1 | No. of Images for Dataset-2 |
---|---|---|
Normal | 1500 | 1200 |
COVID-19 | 1500 | 1200 |
Viral pneumonia | 1500 | 1200 |
Bacterial pneumonia | - | 1200 |
Tuberculosis (Tb) | 1500 | 1200 |
Pneumothorax | - | 1200 |
Total number of images | 6000 | 7200 |
Classes | Diseases |
---|---|
Two-class classifier | Normal and COVID-19 |
Three-class classifier | Normal, COVID-19 and viral pneumonia |
Four-class classifier | Normal, COVID-19, viral pneumonia and Tb |
Five-class classifier | Normal, COVID-19, bacterial pneumonia, Tb, and pneumothorax |
Six-class classifier | Normal, COVID-19, bacterial pneumonia, viral pneumonia, Tb and pneumothorax |
True Label | Predicted Label | ||
(No. of Images = 600) | Normal | COVID-19 | |
Normal | 300 | 0 | |
COVID-19 | 1 | 299 |
Performance Metrics | Achieved Score (%) |
---|---|
Accuracy | 99.833 |
F2-Score | 99.831 |
Precision | 99.833 |
Recall | 99.831 |
Specificity | 99.834 |
True Label | Predicted Label | |||
(No. of Images = 900) | Normal | COVID-19 | Viral Pneumonia | |
Normal | 291 | 0 | 9 | |
COVID-19 | 1 | 297 | 2 | |
Viral pneumonia | 5 | 0 | 295 |
Performance Matrices | Achieved Score (%) |
---|---|
Accuracy | 98.111 |
F2-Score | 98.114 |
Precision | 98.128 |
Recall | 98.111 |
Specificity | 99.055 |
True Label | Predicted Label | ||||
(No. of Images = 1200) | Normal | COVID-19 | Viral Pneumonia | Tb | |
Normal | 291 | 0 | 7 | 2 | |
COVID-19 | 1 | 294 | 1 | 4 | |
Viral pneumonia | 13 | 4 | 283 | 0 | |
Tb | 0 | 4 | 0 | 296 |
Performance Metrics | Achieved Score (%) |
---|---|
Accuracy | 97.000 |
F2-Score | 97.001 |
Precision | 97.006 |
Recall | 97.000 |
Specificity | 99.000 |
True Label | Predicted Label | |||||
(No. of Images = 1200) | Normal | COVID-19 | Pneumothorax | Bacterial Pneumonia | Tb | |
Normal | 234 | 0 | 0 | 1 | 5 | |
COVID-19 | 0 | 230 | 10 | 0 | 0 | |
Pneumothorax | 0 | 25 | 213 | 1 | 1 | |
Bacterial pneumonia | 13 | 3 | 0 | 222 | 2 | |
Tb | 2 | 1 | 0 | 0 | 237 |
Performance Metrics | Achieved Score (%) |
---|---|
Accuracy | 94.666 |
F2-Score | 94.698 |
Precision | 94.827 |
Recall | 94.666 |
Specificity | 98.666 |
True Label | Predicted Label | ||||||
No of Images = 1440 | Normal | COVID-19 | Tb | Bacterial Pneumonia | Viral Pneumonia | Pneumothorax | |
Normal | 227 | 1 | 3 | 1 | 8 | 0 | |
COVID-19 | 0 | 212 | 1 | 0 | 2 | 25 | |
Tb | 0 | 1 | 237 | 0 | 0 | 2 | |
Bacterial pneumonia | 6 | 0 | 0 | 170 | 62 | 2 | |
Viral pneumonia | 5 | 2 | 0 | 51 | 182 | 0 | |
Pneumothorax | 0 | 8 | 3 | 0 | 0 | 229 |
Performance Metrics | Achieved Score (%) |
---|---|
Accuracy | 87.291 |
F2-Score | 87.304 |
Precision | 87.356 |
Recall | 87.291 |
Specificity | 97.458 |
Models | Two-Class Classifier | Three-Class Classifier | Four-Class Classifier | Five-Class Classifier | Six-Class Classifier |
---|---|---|---|---|---|
DenseNet201 | 99.50 | 98.11 | 96.75 | 94.66 | 87.29 |
DenseNet169 | 99.83 | 97.33 | 96.75 | 94.25 | 84.16 |
DenseNet121 | 99.50 | 96.88 | 96.00 | 91.41 | 85.34 |
VGG16 | 99.83 | 97.77 | 95.49 | 93.58 | 84.79 |
VGG19 | 99.83 | 97.66 | 97.00 | 94.41 | 84.72 |
ResNet50 | 97.33 | 85 | 82.16 | 66.50 | 64.72 |
ResNet101 | 90.66 | 86.44 | 79.66 | 75.33 | 65.83 |
NasNet-Mobile | 99.33 | 96 | 95.33 | 93.33 | 84.30 |
MobileNet_v2 | 99.66 | 97.77 | 96.75 | 93.66 | 81.38 |
Publications | No of Classes | Dataset | Accuracy |
---|---|---|---|
Chowdhury et al. [4] | 2 | Unbalanced | 99.7% |
_____________ | _________ | ||
3 | 97.95% | ||
Ozturk et al. [20] | 2 | Unbalanced | 98.08% |
_____________ | _________ | ||
3 | 87.02% | ||
Panwar et al. [23] | 2 | 2 | 88% |
Khan et al. [24] | 2 | Unbalanced | 99% |
_____________ | _________ | ||
3 | 95% | ||
_____________ | _________ | ||
4 | 89.6% | ||
Wang et al. [26] | 2 | Unbalanced | 93.3% |
Narin et al. [14] | 2 | Unbalanced | 96.1% |
Ismael et al. [27] | 2 | Unbalanced | 94.7% |
Proposed work (DenseNet169) | 2 | Balanced | 99.83% |
Proposed work (DenseNet201) | 3 | Balanced | 98.11% |
Proposed work (VGG19) | 4 | Balanced | 97% |
Proposed work (DenseNet169) | 5 | Balanced | 94.66% |
Proposed work (DenseNet201) | 6 | Balanced | 87.29% |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Naseem, M.T.; Hussain, T.; Lee, C.-S.; Khan, M.A. Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning. Sensors 2022, 22, 7977. https://doi.org/10.3390/s22207977
Naseem MT, Hussain T, Lee C-S, Khan MA. Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning. Sensors. 2022; 22(20):7977. https://doi.org/10.3390/s22207977
Chicago/Turabian StyleNaseem, Muhammad Tahir, Tajmal Hussain, Chan-Su Lee, and Muhammad Adnan Khan. 2022. "Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning" Sensors 22, no. 20: 7977. https://doi.org/10.3390/s22207977
APA StyleNaseem, M. T., Hussain, T., Lee, C.-S., & Khan, M. A. (2022). Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning. Sensors, 22(20), 7977. https://doi.org/10.3390/s22207977