Hybrid Artificial Intelligence Approach to COVID-19 Diagnosis from CT Images: Deep Networks and Classification Analysis

Yıl 2024, Cilt: 5 Sayı: 1, 24 - 32

Muhammed Alperen Horoz Seda Arslan Tuncer Çağla Danacı

https://doi.org/10.55195/jscai.1467768

Öz

Using lung images obtained by computed tomography (CT), this study aims to detect coronavirus (Covid-19) disease with deep learning (DL) techniques. The study included 751 lung CT images from 118 Covid-19 patients and 628 lung CT images from 100 healthy individuals. In total, 70% of the 1379 images were used for training and 30% for testing. In the study, two different methods were proposed on the same dataset. In the first method, the images were trained on AlexNet, VGG-16, VGG-19, GoogleNet and a proposed network. The performance metrics obtained from the five networks were compared and it was observed that the proposed network achieved the highest accuracy value with 95.61%. In the second method, the images were trained on VGG-16, VGG-19, DenseNet-121, ResNet-50 and MobileNet networks. Among the image features obtained from each of these networks, the best 1000 features were selected by Principal Component Analysis (PCA). The best 1000 features were classified with Random Forest (RF) and Support Vector Machines (SVM). According to the classification results, the best 1000 features selected from the features extracted by the VGG-16 and MobileNet networks were obtained with the highest accuracy rate of 93.94% using SVM. It is thought that this study can be a helpful tool in the diagnosis of Covid-19 disease while reducing time and labor costs with the use of artificial intelligence (AI).

Anahtar Kelimeler

Covid-19, Deep Learning, Classification, Feature Selection, Computed Tomography

Kaynakça

• Wikipedia. (2022, June 19). COVID-19. Retrieved on: February 9, 2023, https://tr.wikipedia.org/wiki/COVID-19
• World Health Organization. (2020, March 11). WHO President's keynote speech on COVID-19 - March 11, 2020. Retrieved on: February 9, 2023, https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
• Worldometer. (2022, September 19). Coronavirus. Retrieved on: February 9, 2023, https://www.worldometers.info/coronavirus/
• Liu, J., Zheng, X., Tong, Q., Li, W., Wang, B., Sutter, K., ... & Zhao, Z. (2020). Overlapping and discrete aspects of the pathology and pathogenesis of the emerging human pathogenic coronaviruses SARS-CoV, MERS-CoV, and 2019-nCoV. Journal of Medical Virology, 92(5), 491-494.
• Population Europe. (2022, September 20). COVID-19 and Demographic Change. Retrieved on: February 9, 2023, https://population-europe.eu/files/documents/pb25_covid.pdf
• Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., ... & Cao, B. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet, 395(10229), 1054-1062.
• Güner, R., Hasanoğlu, İ., & Aktaş, F. (2020). COVID-19: Prevention and control measures in community. Turkish Journal of Medical Sciences, 50, 571-577.
• Weissleder, R., Lee, H., Ko, J., & Pittet, M.J. (2020). COVID-19 diagnostics in context. Science Translational Medicine, 12(546), eabc1931. https://doi.org/10.1126/scitranslmed.abc1931
• Radiology Business. (2021, April 14). Clinicians use lung ultrasound to quickly triage coronavirus patients. Retrieved on: February 9, 2023, https://www.radiologybusiness.com/topics/care-delivery/ultrasound-coronavirus-covid-19-x-ray-ct-scan-radiology
• Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European Radiology, 31(8), 6096-6104. https://doi.org/10.1007/s00330-021-07715-1
• Wang, S., Zha, Y., Li, W., Wu, Q., Li, X., Niu, M., Wang, M., Qiu, X., Li, H., Yu, H., Gong, W., Bai, Y., Li, L., Zhu, Y., Wang, L., & Tian, J. (2020). A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. European Respiratory Journal, 56(2), 2000775. https://doi.org/10.1183/13993003.00775-2020
• Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., Cao, K., Liu, D., Wang, G., Xu, Q., Fang, X., Zhang, S., & Xia, J. (2020). Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy. Radiology, 296(2), E65-E71. https://doi.org/10.1148/radiol.2020200905
• Kassania, S. H., Kassanib, P. H., Wesolowskic, M. J., Schneidera, K. A., & Detersa, R. (2021). Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach. Biocybernetics and biomedical engineering, 41(3), 867–879. https://doi.org/10.1016/j.bbe.2021.05.013
• Özkaya, U., Öztürk, Ş., & Barstugan, M. (2020). Coronavirus (COVID-19) classification using deep features fusion and ranking technique. In Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach (pp. 281-295).
• Farooq, M., & Hafeez, A. (2020). Covid-resnet: A deep learning framework for screening of COVID-19 from radiographs. arXiv preprint arXiv:2003.14395.
• Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Wang, R., Zhao, H., Chong, Y., Shen, J., Zha, Y., Yang, Y. (2021). Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2775-2780. https://doi.org/10.1109/TCBB.2021.3065361
• Horoz, M. A. (2023). Bilgisayarlı tomografi görüntüleri kullanılarak COVID-19 hastalığının tanısı için derin öğrenme yöntemlerinin kullanılması, Master Thesis, Firat University, Faculty of Engineering, Software Engineering.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1-9). https://doi.org/10.1109/CVPR.2015.7298594
• Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations, ICLR 2015 (pp. 1-14).
• Zheng, Y., Yang, C., & Merkulov, A. (2018). Breast cancer screening using convolutional neural network and follow-up digital mammography. In Computational Imaging III (Vol. 10669, p. 1066905).
• Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4700-4708).
• Kumar, R. (2019). Adding binary search connections to improve densenet performance. In 5th International Conference on Next Generation Computing Technologies.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
• Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv preprint arXiv:1704.04861.
• Cañeque, V., Perez, C., Velasco, S., Diaz, M. T., Lauzurica, S., Alvarez, I., ... & De la Fuente, J. (2004). Principal Component Analysis of Carcass and Meat Quality of Light Lambs. Meat Science, 67, 595-605.
• Jolliffe, I. T. (2003). Principal component analysis. Technometrics, 45(3), 276.
• Breiman, L. (2001). Random forest. Machine Learning, 45, 5-32.
• Çomak, E. (2008). Destek Vektör Makinelerinin Etkin Eğitimi İçin Yeni Yaklaşımlar, PhD Thesis, Selçuk University, Institute of Science and Technology, Konya.
• Attallah, O. (2023). RADIC: A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics. Chemometrics and Intelligent Laboratory Systems, 233, 104750.
• Alaiad, A. I., Mugdadi, E. A., Hmeidi, I. I., Obeidat, N., & Abualigah, L. (2023). Predicting the severity of COVID-19 from lung CT images using novel deep learning. Journal of medical and biological engineering, 43(2), 135-146.