A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm
Abstract SARS-CoV-2 has caused a severe pandemic worldwide. This virus appeared at the end of 2019. This virus causes respiratory distress syndrome. Computed tomography (CT) imaging provides important radiological information in the diagnosis and clinical evaluation of pneumonia caused by bacteria or a virus. CT imaging is widely utilized in the identification and evaluation of COVID-19. It is an important requirement to establish diagnostic support systems using artificial intelligence methods to alleviate the workload of healthcare systems and radiologists due to the disease. In this context, an important study goal is to determine the clinical severity of the pneumonia caused by the disease. This is important for determining treatment procedures and the follow-up of a patient’s condition. In the study, automatic COVID-19 severity classification was performed using three-class (mild, moderate, and severe) and two-class (non-severe and severe). In the study, deep learning models were used for classification. Also, CT images were utilized as radiological images. A total of 483 COVID-19 CT-image slices, 267 mild, 156 moderate, and 60 severe, were used. These images and labels were used directly for the three classifications. In the two-class classification, the mild and moderate images were accepted as non-severe. A total of eight classifications were made with convolutional neural network (CNN) architectures. These architectures are MobileNetv2, ResNet101, Xception, Inceptionv3, GoogleNet, EfficientNetb0, DenseNet201, and DarkNet53. In the study, the results of the top four CNN architectures with the best performance were combined using a pipeline algorithm. In this way, it is seen that significant improvements have been achieved in the results of the study. Before using the pipeline algorithm for the three-class classification, the results of weighted recall-sensitivity (SNST), specificity (SPCF), accuracy (ACCR), F-1 score (F-1), area under the receiver operating characteristic curve (AUC), and overall ACCR were obtained: 0.7785, 0.8351, 0.8299, 0.7758, 0.9112 and 0.7785, respectively. After using the pipeline algorithm for the three-class classification, the results of these parameters were obtained: 0.8095, 0.8555, 0.8563, 0.8076, 0.9089, and 0.8095, respectively. Before using the pipeline algorithm for the two-class classification, the results of SNST, SPCF, ACCR, F-1, and AUC were obtained: 0.9740, 0.8500, 0.9482, 0.9703, and 0.9788, respectively. After using the pipeline algorithm for the two-class classification, the results of these parameters were obtained: 0.9811, 0.8333, 0.9627, 0.9788, and 0.9851, respectively.
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Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. IEEE conference on computer vision and pattern recognition (CVPR), pp. 1251–1258. IEEE. 10.1109/CVPR.2017.195
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. IEEE conference on computer vision and pattern recognition (CVPR), pp. 770–778. IEEE. 10.1109/CVPR.2016.90
Ho, T., Park, J., Kim, T., Park, B., Lee, J., Kim, J. Y., & Choi, S. (2021). Deep learning models for predicting severe progression in COVID-19-infected patients: Retrospective study. JMIR medical informatics, 9(1), e24973. 10.2196/24973
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., & Cao, B., (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet, 395(10223), 497–506. 10.1109/CVPR.2017.243
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700–4708. IEEE. 10.1109/CVPR.2017.243
Huang, Z., Liu, X., Wang, R. Zhang, M., Zeng, X., Liu, J., & Hu, Z. (2021). FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms. Applied Intelligence, 51(5), 2838–2849. 10.1007/s10489-020-01965-0
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Li, Z., Zhong, Z., Li, Y., Zhang, T., Gao, L., Jin, D., & Tang, Y. (2020b). From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans. European radiology, 30(12), 6828–6837. 10.1007/s00330-020-07042-x
Lin, L., Fu, G., Chen, S., Tao, J., Qian, A., Yang, Y., & Wang, M. (2021). CT manifestations of coronavirus disease (COVID-19) pneumonia and influenza virus pneumonia: A comparative study. American Journal of Roentgenology, 216(1), 71–79. 10.2214/AJR.20.23304
Meng, L., Dong, D., Li, L., Niu, M., Bai, Y., Wang, M., & Tian, J. (2020). A deep learning prognosis model help alert for COVID-19 patients at high-risk of death: a multi-center study. IEEE journal of biomedical and health informatics, 24(12), 3576–3584. 10.1109/JBHI.2020.3034296
Pianura, E., Di Stefano, F., Cristofaro, M., Petrone, A., Fusco, N., Albarello, F., & Schininà, V. (2020). Coronavirus-HKU1 Pneumonia and Differential Diagnosis with COVID-19. Radiology: Cardiothoracic Imaging, 2(2), e200162. 10.1148/ryct.2020200162
Pontone, G., Scafuri, S., Mancini, M. E., Agalbato, C., Guglielmo, M., Baggiano, A., & Rossi, A. (2021). Role of computed tomography in COVID-19. Journal of cardiovascular computed tomography, 15(1), 27–36. 10.1016/j.jcct.2020.08.013
Redmon, J. (2018). Darknet: Open Source Neural Networks in C; 2016. http://pjreddie.com/darknet.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. IEEE conference on computer vision and pattern recognition, 4510–4520. IEEE. 10.1109/CVPR.2018.00474
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., & Rabinovich, A. (2015). Going deeper with convolutions. IEEE conference on computer vision and pattern recognition, 1–9. IEEE. 10.1109/CVPR.2015.7298594
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. IEEE conference on computer vision and pattern recognition, 2818–2826. IEEE. 10.1109/CVPR.2016.308
Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, 6105–6114. PMLR.
Wong, M. D., Thai, T., Li, Y., & Liu, H. (2020). The role of chest computed tomography in the management of COVID-19: A review of results and recommendations. Experimental Biology and Medicine, 245(13), 1096–1103. 10.1177/1535370220938315
World Health Organization (WHO). (2022a). World experts and funders set priorities for COVID-19 research. https://www.who.int/newsroom/.
World Health Organization (WHO). (2022b). WHO Coronavirus (COVID-19) Dashboard. https://covid19.who.int/.
World Health Organization (WHO). (2022c). COVID-19 vaccines. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/covid-19-vaccines.
Xiao, L. S., Li, P., Sun, F., Zhang, Y., Xu, C., Zhu, H., & Zhu, H. (2020). Development and validation of a deep learning-based model using computed tomography imaging for predicting disease severity of coronavirus disease 2019. Frontiers in bioengineering and biotechnology, (8), 898. 10.3389/fbioe.2020.00898
Yasar, H., & Ceylan, M. (2021a). A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks. Applied Intelligence, 51(5), 2740–2763. 10.1007/s10489-020-02019-1
Yasar, H., & Ceylan, M. (2021b). Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images. Cognitive Computation, 1–28. 10.1007/s12559-021-09915-9
Yu, Z., Li, X., Sun, H., Wang, J., Zhao, T., Chen, H., & Xie, Z. (2020). Rapid identification of COVID-19 severity in CT scans through classification of deep features. BioMedical Engineering OnLine, 19(1), 1–13.
Zhao, D., Zheng, F. Y. L. W. L., Guo, Y. G. J. Y. F., & Gao, H. Z. R. (2020). Comparative study on the clinical features of COVID-19 pneumonia to other pneumonias. Clinical Infectious Diseases, 71(15), 756–761.
Zhu, X., Song, B., Shi, F., Chen, Y., Hu, R., Gan, J., & Shen, D. (2021). Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan. Medical image analysis, 67, 101824.
Yaşar, H., Ceylan, M., Cebeci, H., Kılınçer, A., Seher, N., Kanat, F., & Koplay, M. (2023). A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e28715. https://doi.org/10.14201/adcaij.28715
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