Improving the Diagnosis of COVID-19 by using a combination of Deep Learning Models

Zabbah, I.; Layeghi, K.; Ebrahimpour, Reza

doi:10.22061/jecei.2022.8200.491

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	Improving the Diagnosis of COVID-19 by using a combination of Deep Learning Models
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 14، دوره 10، شماره 2، مهر 2022، صفحه 411-424 اصل مقاله (1.24 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2022.8200.491
نویسندگان
I. Zabbah^* ¹؛ K. Layeghi²؛ Reza Ebrahimpour³
¹Department of Computer Engineering, Islamic Azad University of Tehran North Branch, Tehran, Iran.
²Department of Computer Engineering, Islamic Azad University of North Tehran Branch, Tehran, Iran.
³Artificial Intelligence Department, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
تاریخ دریافت: 12 آبان 1400، تاریخ بازنگری: 24 بهمن 1400، تاریخ پذیرش: 25 بهمن 1400
چکیده
Background and Objectives: COVID-19 disease still has a devastating effect on society health. The use of X-ray images is one of the most important methods of diagnosing the disease. One of the challenges specialists are faced is no diagnosing in time. Using Deep learning can reduce the diagnostic error of COVID-19 and help specialists in this field. Methods: The aim of this study is to provide a method based on a combination of deep learning(s) in parallel so that it can lead to more accurate results in COVID-19 disease by gathering opinions. In this research, 4 pre-trained (fine-tuned) deep model have been used. The dataset of this study is X-ray images from Github containing 1125 samples in 3 classes include normal, COVID-19 and pneumonia contaminated. Results: In all networks, 70% of the samples were used for training and 30% for testing. To ensure accuracy, the K-fold method was used in the training process. After modeling and comparing the generated models and recording the results, the accuracy of diagnosis of COVID-19 disease showed 84.3% and 87.2% when learners were not combined and experts were combined respectively. Conclusion: The use of machine learning techniques can lead to the early diagnosis of COVID-19 and help physicians to accelerate the healing process. This study shows that a combination of deep experts leads to improved diagnosis accuracy.
کلیدواژه‌ها
COVID-19؛ Deep Networks؛ Data Mining؛ Machine Learning؛ Mixture of Experts

مراجع
[1] Z. Wu, J.M. McGoogan, "Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention," Jama, 323(13): 1239-1242, 2020. [2] W. Kong, P.P. Agarwal, "Chest imaging appearance of COVID-19 infection, radiology," Radiol. Cardiothorac. Imaging, 2(1): 1-4, 2020. [3] T. Lancet, Editorial COVID-19: too little, too late? The Lancet 395(10226): 755, 2020. [4] M.S. Razai, K. Doerholt, S. Ladhani, P. Oakeshott, “Coronavirus disease 2019 (COVID-19): a Guide for UK GPs” BMJ, 368(800): 1-5, 2020. [5] X.Peng, X. Xu, Y. Li, L. Cheng, X. Zhou, B. Ren, "Transmission routes of 2019-nCoV and controls in dental practice,” Int. J. oral sci., 12(1): 1-6, 2020. [6] M.Togaçar, B. Ergen, Z. Comert, "Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders," Med. Hypotheses, 135, 2020. [7] X. Liu, Z. Deng, Y. Yang, Liu, Xiaolong, Zhidong Deng, Yuhan Yang, "Recent progress in semantic image segmentation," Artif. Intell. Rev., 52(2): 1089-106, 2019. [8] M. Zhang, X. H. Wang, Y. L. Chen, K. L. Zhao, Y. Q. Cai, C. L. An, M. G. Lin, X. D. Mu, "Clinical features of 2019 novel coronavirus pneumonia in the early stage from a fever clinic in Beijing," Zhonghua Jie He He Hu Xi Za Zhi, 43(0): 215-218, 2020. [9] M. Zreik, N. Lessmann, R.W. Van Hamersvelt, J.M. Wolterink, M. Voskuil, M.A. Viergever, T. Leiner, I. sgum, "Deep learning analysis of the myocardium in coronary ct angiography for identifcation of patients with functionally significant coronary artery stenosis," Med. Image Anal., 44 (1): 72-85, 2018. [10] G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A. Van Der Laak, B. Van Ginneken, C.I. Sanchez, "A survey on deep learning in medical image analysis," Med. Image Anal. 42(1): 60-88, 2017. [11] S. Lakshmanaprabu, S.N. Mohanty, K. Shankar, N. Arunkumar, G. Ramirez, "Optimal deep learning model for classifcation of lung cancer on ct images," Future Generat. Comput. Syst., 9(2): 374–82, 2019. [12] J.Z. Cheng, Y.H. Chou, J. Qin, C.M. Tiu, Y.C Chang, C.S Huang, D. Shen, C.M. Chen, "Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans," Sci. rep., 6(1): 1–13, 2016. [12] A.K. Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna, J.J.P.C. Rodrigues, "Identifying pneumonia in chest X-rays: a deep learning approach", Measurement, 145: 511–518,2019. [13] P. An, H. Chen, X. Jiang, J. Su, Y. Xiao, Y. Ding, H. Ren, M. Ji, Y. Chen, W. Chen, et al., “Clinical characteristics of coronavirus disease (COVID-19) patients with gastrointestinal symptoms: A report of 164 cases,” Dig. Liv. Dis., 52(10): 1076-1079, 2020. [14] M. Sherief, "Hepatic and gastrointestinal involvement in coronavirus disease (COVID-19): What do we know till now," Arab Gastroenterol., 21(1): 3-8. (2020). [15] I.M. Baltruschat, H. Nickisch, M. Grass, T. Knopp, A. Saalbach, "Comparison of deep learning approaches for multi-label chest X-ray classification," Sci. rep., 9(1): 1-10, 2019. [16] E.E.D. Hemdan, M.A. Shouman, M.E. Karar, "A framework of deep learning classifiers to diagnose COVID-19 in X-Ray images," arXiv preprint arXiv:2003.11055, 2020. [17] S.S. Yadav, S.M. Jadhav, “Deep convolutional neural network based medical image classification for disease diagnosis,” J. Big Data, 6(1): 1-18, 2019. [18] S.Wang, B. Kang, J. Ma, X Zeng, M. Xiao, J. Guo, M. Cai, J. Yang, Y. Li, X. Meng, et al., “A deep learning algorithm using ct images to screen for corona virus disease (COVID-19),” Eur. Radiol., 31(8): 6096-6104, 2021. [19] O. Gozes, M. Frid-Adar, H. Greenspan, P.D. Browning, H. Zhang, W. Ji, A. Bernheim, E. Siegel, " Rapid ai development cycle for the coronavirus (COVID-19) pandemic: initial results for automated detection & patient monitoring using deep learning ct image analysis," arXiv:2003.05037; 1-22, 2020. [20] L. Wang, ZQ. Lin, A. Wong, "COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images," arXiv preprint arXiv: 2003.09871. [21] L.Wang, A. Wong, "A tailored deep convolutional neural network design for detection of covid-19 cases from chest radiography images," J. Network Comput. Appl. 20(1): 1-12, 2020. [22] A. Laghi, "Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence," Lancet Digital Health 2(5): e225, 2020. [23] K.S. Lee, J.Y. Kim, E.T. Jeon, W. Choi, N. Kim, K. Lee, "Evaluation of scalability and degree of fine-tuning of deep convolutional neural networks for COVID-19 screening on chest X-ray images using explainable deep-learning algorithm," J. Pers. Med., 10(4), 213, 2019. [24] J.P. Cohen, P. Morrison, L. Dao, “COVID-19 Image Data Collection,” arXiv:2003.11597, 2020. [25] J. Zhang, Y. Xie, Y. Li, C. Shen, Y. Xia, "COVID-19 screening on chest x-ray images using deep learning based anomaly detection," arXiv preprint arXiv:2003.12338 [26] A. Akbari, H. Farsi, S. Mohamadzadeh, "Deep neural network with extracted features for social group detection," J. Electr. Comput. Eng. Innovations (JECEI), 9(1): 47-56, 2021. [27] F. Ucar, U. Korkmaz, M. Ferhat, K. Deniz. "COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease (COVID-19) from X-ray images," Med. Hypotheses 140: 109761, 2020. [28] A. Krizhevsky, l. Sutskever, G. Hinton, “Imagenet classification with deep convolutional neural networks” part of Advances in Neural Information Processing Systems 25, 1097-1105, 2012. [29] C. Szegedy, W. Liu,Y. Jia, P. Sermanet, S. Reed, D. Anguelov, A. Rabinovich, “Going deeper with convolutions,” in Proc. the IEEE conference on computer vision and pattern recognition: 1-9, 2015. [30] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proc. the IEEE conference on computer vision and pattern recognition: 2818-2826, 2016. [31] J. P. Cohen, COVID-19 Image Data Collection, 2020. [32] J. Khosravi, M. Shams Esfandabadi, R. Ebrahimpour, “Image registration based on sum of square difference cost function,” J. Electr. Comput. Eng. Innovations (JECEI), 6(2): 273-281, 2018. [33] T. Zhou, H. Lu, Z. Yang, S. Qiu, B. Huo, Y. Dong, "The ensemble deep learning model for novel COVID-19 on CT images," Appl. Soft Comput. 98: 106885, 2021. [34] E. Pazouki, M. Rahmati, "A variational level set approach to multiphase multi-object tracking in camera network base on deep features," J. Electr. Comput. Eng. Innovations (JECEI), 9(2): 203-214, 2021. [35] S. Masoudnia, R. Ebrahimpour, “Mixture of experts: a literature survey,” Artif. Intell. Rev., 42(2): 275-293, 2014.
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Improving the Diagnosis of COVID-19 by using a combination of Deep Learning Models