International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 15, No. 1, 8 Feb. 2023
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A Model based on Deep Learning for COVID-19 X-rays Classification
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Index Terms
COVID-19, X-rays, Deep Transfer Learning, VGG19, Classification, Machine learning, Support Vector Machine (SVM).
Abstract
Throughout the COVID-19 pandemic in 2019 and until now, patients overrun hospitals and health care emergency units to check up on their health status. The health care systems were burdened by the increased number of patients and there was a need to speed up the diagnoses process of detecting this disease by using computer algorithms. In this paper, an integrated model based on deep and machine learning for covid-19 x-rays classification will be presented. The integration is built-up open two phases. The first phase is features extraction using deep transfer models such as Alexnet, Resnet18, VGG16, and VGG19. The second phase is the classification using machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, and Ensemble algorithm. The dataset selected consists of three classes (COVID-19, Viral pneumonia, and Normal) class and the dataset is available online under the name COVID-19 Radiography database. More than 30 experiments are conducted to select the optimal integration between machine and deep learning models. The integration of VGG19 and SVM achieved the highest accuracy possible with 98.61%. The performance indicators such as Recall, Precision, and F1 Score support this finding. The proposed model consumes less time and resources in the training process if it is compared to deep transfer models. Comparative results are con-ducted at the end of the research, and the proposed model overcomes related works which used the same dataset in terms of testing accuracy.
Cite This Paper
Eman I. Abd El-Latif, Nour Eldeen Khalifa, "A Model based on Deep Learning for COVID-19 X-rays Classification", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.1, pp. 36-46, 2023. DOI:10.5815/ijigsp.2023.01.04
Reference
[1]S. Law, A. W. Leung, and C. Xu, “Severe acute respiratory syndrome (SARS) and coronavirus disease-2019 (COVID-19): From causes to preventions in Hong Kong,” International Journal of Infectious Diseases, vol. 94, pp. 156–163, May 2020, doi: 10.1016/j.ijid.2020.03.059.
[2]P.-I. Lee and P.-R. Hsueh, “Emerging threats from zoonotic coronaviruses-from SARS and MERS to 2019-nCoV,” Journal of Microbiology, Immunology and Infection, vol. 53, no. 3, pp. 365–367, Jun. 2020, doi: 10.1016/j.jmii.2020.02.001.
[3]D. Han, Q. Liu, and W. Fan, “A new image classification method using CNN transfer learning and web data augmentation,” Expert Systems with Applications, vol. 95, pp. 43–56, Apr. 2018, doi: 10.1016/j.eswa.2017.11.028.
[4]R. H. Abiyev and M. K. S. Ma’aitah, “Deep Convolutional Neural Networks for Chest Diseases Detection,” Journal of Healthcare Engineering, vol. 2018, pp. 1–11, Aug. 2018, doi: 10.1155/2018/4168538.
[5]B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Włoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS ONE, vol. 12, no. 9, p. e0184554, Sep. 2017, doi: 10.1371/journal.pone.0184554.
[6]O. K. Oyedotun, E. O. Olaniyi, A. Helwan, and A. Khashman, “Hybrid auto encoder network for iris nevus diagnosis considering potential malignancy,” in 2015 International Conference on Advances in Biomedical Engineering (ICABME), Beirut, Lebanon, Sep. 2015, pp. 274–277. doi: 10.1109/ICABME.2015.7323305.
[7]A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.
[8]F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,” arXiv:1602.07360 [cs], Nov. 2016, Accessed: Jan. 18, 2022. [Online]. Available: http://arxiv.org/abs/1602.07360
[9]K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv:1409.1556 [cs], Apr. 2015, Accessed: Jan. 18, 2022. [Online]. Available: http://arxiv.org/abs/1409.1556
[10]J. Qiu et al., “Going Deeper with Embedded FPGA Platform for Convolutional Neural Network,” in Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey California USA, Feb. 2016, pp. 26–35. doi: 10.1145/2847263.2847265.
[11]H. Qin, R. Gong, X. Liu, X. Bai, J. Song, and N. Sebe, “Binary neural networks: A survey,” Pattern Recognition, vol. 105, p. 107281, Sep. 2020, doi: 10.1016/j.patcog.2020.107281.
[12]D. S. Kermany et al., “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell, vol. 172, no. 5, pp. 1122-1131.e9, Feb. 2018, doi: 10.1016/j.cell.2018.02.010.
[13]S. Rajaraman, S. Candemir, I. Kim, G. Thoma, and S. Antani, “Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs,” Applied Sciences, vol. 8, no. 10, p. 1715, Sep. 2018, doi: 10.3390/app8101715.
[14]A. K. Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna, and J. J. P. C. Rodrigues, “Identifying pneumonia in chest X-rays: A deep learning approach,” Measurement, vol. 145, pp. 511–518, Oct. 2019, doi: 10.1016/j.measurement.2019.05.076.
[15]P. Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” arXiv:1711.05225 [cs, stat], Dec. 2017, Accessed: Jan. 18, 2022. [Online]. Available: http://arxiv.org/abs/1711.05225
[16]K. Ahmed, S. Abdelghafar, A. Salama, N. E. M. Khalifa, A. Darwish, and A. E. Hassanien, “Tracking of COVID-19 Geographical Infections on Real-Time Tweets.” 2021.
[17]A. El-Aziz, A. Atrab, N. E. M. Khalifa, A. Darwsih, and A. E. Hassanien, “The role of emerging technologies for combating COVID-19 pandemic,” in Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches, Springer, 2021, pp. 21–41.
[18]A. A. Abd El-Aziz, N. E. M. Khalifa, and A. E. Hassanien, “Exploring the Impacts of COVID-19 on Oil and Electricity Industry,” in The Global Environmental Effects During and Beyond COVID-19, Springer, 2021, pp. 149–161.
[19]N. E. KHALIFA, G. MANOGARAN, M. H. N. TAHA, and M. LOEY, “The classification of possible Coronavirus treatments on a single human cell using deep learning and machine learning approaches,” Journal of Theoretical and Applied Information Technology, vol. 99, no. 21, 2021.
[20]N. E. M. Khalifa, G. Manogaran, M. H. N. Taha, and M. Loey, “A deep learning semantic segmentation architecture for COVID-19 lesions discovery in limited chest CT datasets,” Expert Systems, p. e12742, 2021.
[21]N. E. M. Khalifa, F. Smarandache, G. Manogaran, and M. Loey, “A study of the neutrosophic set significance on deep transfer learning models: An experimental case on a limited covid-19 chest x-ray dataset,” Cognitive Computation, pp. 1–10, 2021.
[22]N. E. M. Khalifa, M. H. N. Taha, A. E. Hassanien, and S. H. N. Taha, “The Detection of COVID-19 in CT Medical Images: A Deep Learning Approach,” in Big Data Analytics and Artificial Intelligence against COVID-19: Innovation Vision and Approach, Springer, 2020, pp. 73–90.
[23]T. D. Pham, “Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?,” Health Inf Sci Syst, vol. 9, no. 1, p. 2, Dec. 2021, doi: 10.1007/s13755-020-00135-3.
[24]M. M. Taresh, N. Zhu, T. A. A. Ali, A. S. Hameed, and M. L. Mutar, “Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks,” International Journal of Biomedical Imaging, vol. 2021, pp. 1–9, May 2021, doi: 10.1155/2021/8828404.
[25]S. V J and J. F. D, “Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images,” Computational and Mathematical Methods in Medicine, vol. 2021, pp. 1–10, Nov. 2021, doi: 10.1155/2021/9269173.
[26]A. I. Khan, J. L. Shah, and M. M. Bhat, “CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images,” Computer Methods and Programs in Biomedicine, vol. 196, p. 105581, Nov. 2020, doi: 10.1016/j.cmpb.2020.105581.
[27]D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, “Pneumonia Detection Using CNN based Feature Extraction,” in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, Feb. 2019, pp. 1–7. doi: 10.1109/ICECCT.2019.8869364.
[28]E. Ayan and H. M. Unver, “Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning,” in 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, Apr. 2019, pp. 1–5. doi: 10.1109/EBBT.2019.8741582.
[29]V. Chouhan et al., “A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images,” Applied Sciences, vol. 10, no. 2, p. 559, Jan. 2020, doi: 10.3390/app10020559.
[30]F. Ahmad, A. Farooq, and M. U. Ghani, “Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images,” Computational Intelligence and Neuroscience, vol. 2021, pp. 1–17, Jan. 2021, doi: 10.1155/2021/8890226.
[31]“COVID-19 Radiography Database.” Accessed: Jan. 18, 2022. [Online]. Available: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
[32]Y. Zhang, “Support Vector Machine Classification Algorithm and Its Application,” in Information Computing and Applications, vol. 308, C. Liu, L. Wang, and A. Yang, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 179–186. doi: 10.1007/978-3-642-34041-3_27.
[33]L. A. Breslow and D. W. Aha, “Simplifying decision trees: A survey,” The Knowledge Engineering Review, vol. 12, no. 01, pp. 1–40, Jan. 1997, doi: 10.1017/S0269888997000015.
[34]X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, “A survey on ensemble learning,” Front. Comput. Sci., vol. 14, no. 2, pp. 241–258, Apr. 2020, doi: 10.1007/s11704-019-8208-z.
[35]S. Guhathakurata, S. Kundu, A. Chakraborty, and J. S. Banerjee, “A novel approach to predict COVID-19 using support vector machine,” in Data Science for COVID-19, Elsevier, 2021, pp. 351–364. doi: 10.1016/B978-0-12-824536-1.00014-9.
[36]V. Singh et al., “Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 23, no. 8, pp. 1583–1597, Nov. 2020, doi: 10.1080/09720529.2020.1784535.
[37]S. H. Yoo et al., “Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging,” Front. Med., vol. 7, p. 427, Jul. 2020, doi: 10.3389/fmed.2020.00427.
[38]J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189–215, Sep. 2020, doi: 10.1016/j.neucom.2019.10.118.
[39]O. Sagi and L. Rokach, “Ensemble learning: A survey,” WIREs Data Mining Knowl Discov, vol. 8, no. 4, Jul. 2018, doi: 10.1002/widm.1249.
[40]S. R. Safavian and D. Landgrebe, “A survey of decision tree classifier methodology,” IEEE Trans. Syst., Man, Cybern., vol. 21, no. 3, pp. 660–674, Jun. 1991, doi: 10.1109/21.97458.
[41]M. Umair et al., “Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset,” Sensors, vol. 21, no. 17, p. 5813, Aug. 2021, doi: 10.3390/s21175813.