Prabira Kumar Sethy
Department of Electronics, Sambalpur University, Odisha, India.
Santi Kumari Behera
Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Odisha, India.
Pradyumna Kumar Ratha
Department of Computer Science and Engineering, Sambalpur University Institute of Information Technology, Sambalpur University, Odisha, India.
Department of Electronics and Communication Engineering, Dr. C. V. Raman University, Chhattisgarh, India.
The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation. The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose. The SVM classifies the corona affected X-ray images from others. The methodology consists of three categories of Xray images, i.e., COVID-19, pneumonia and normal. The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people. SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models. The SVM produced the best results using the deep feature of ResNet50. The classification model, i.e. ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95.33%,95.33%,2.33% and 95.34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS). Again, the highest accuracy achieved by ResNet50 plus SVM is 98.66%. The result is based on the Xray images available in the repository of GitHub and Kaggle. As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach. Also, a comparison analysis of other traditional classification method is carried out. The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. In traditional image classification method, LBP plus SVM achieved 93.4% of accuracy.
Keywords- Coronavirus, COVID-19, Diagnosis, Deep features, SVM.
Sethy, P. K., Behera, S. K., Ratha, P. K., & Biswas, P. (2020). Detection of Coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine. International Journal of Mathematical, Engineering and Management Sciences, 5(4), 643-651. https://doi.org/10.33889/IJMEMS.2020.5.4.052.
Conflict of Interest
The authors confirm that there is no conflict of interest to declare for this publication.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors sincerely appreciate the editor and reviewers for their time and valuable comments.
Chan, J.F.W., To, K.K.W., Tse, H., Jin, D.Y., & Yuen, K.Y. (2013). Interspecies transmission and emergence of novel viruses: lessons from bats and birds. Trends in Microbiology, 21(10), 544-555.
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., & Yu, T. (2020b). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet, 395(10223), 507-513.
Chen, Y., Liu, Q., & Guo, D. (2020a). Emerging coronaviruses: genome structure, replication, and pathogenesis. Journal of Medical Virology, 92(4), 418-423.
Cohen, J.P., Morrison, P., & Dao, L. (2020). COVID-19 image data collection. arXiv preprint arXiv:2003.11597. Link: https://github.com/ieee8 023/covid-chestxray-dataset.
European Centre for Disease Prevention and Control. An agency of the European Union. Link: https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases.
Greenspan, H., Van Ginneken, B., & Summers, R.M. (2016). Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging, 35(5), 1153-1159.
Kaggle. Link: https://www.kaggle.com/andrewmvd/convid19-X-rays.
Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C., Liang, H., Baxter, S.L., & Dong, J. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122-1131.
Lopes, U.K., & Valiati, J.F. (2017). Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Computers in Biology and Medicine, 89, 135-143.
Nishiura, H., Linton, N.M., & Akhmetzhanov, A.R. (2020). Serial interval of novel coronavirus (COVID-19) infections. International Journal of Infectious Diseases, 93, 284-286.
Perlman, S., & Netland, J. (2009). Coronaviruses post-SARS: update on replication and pathogenesis. Nature Reviews Microbiology, 7(6), 439-450.
Wahab, N., Khan, A., & Lee, Y.S. (2017). Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection. Computers in Biology and Medicine, 85, 86-97.
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., & Zhao, Y. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Jama, 323(11), 1061-1069.
World Health Organization. (2020, April 13). Link: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
Xia, Y., Wulan, N., Wang, K., & Zhang, H. (2018). Detecting atrial fibrillation by deep convolutional neural networks. Computers in Biology and Medicine, 93, 84-92.
Yang, Y., Lu, Q., Liu, M., Wang, Y., Zhang, A., Jalali, N., & Zhang, X. (2020). Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China. medRxiv.