Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması

Year 2021, , 26 - 39, 31.12.2021

Furkan Eryılmaz , Hacer Karacan

https://doi.org/10.29130/dubited.1011829

Cited By: 3

Abstract

Solunum sistemine etki eden ve ileri vakalarda ölüme neden olan korona virüs salgını yaklaşık iki yıldır devam etmektedir. Her ülkenin salgın ile mücadele yöntemi farklı olmasına rağmen ortak izlenen metot ise hastalığın tespiti ve izolasyonudur. Tespit ve izolasyon için en kritik adım ise COVID-19 tanısının doğru ve hızlı konulmasıdır. Akciğer X-Ray görüntülerinde virüse özgü bulgulara rastlanılması, bu verilerin hastalık teşhisinde kullanılabileceğini göstermektedir. İlgili çalışmanın amacı, makine öğrenmesi yöntemleri ile COVID-19 ve diğer akciğer hastalıklarına ait X-Ray görüntülerini işleyerek çoklu sınıflandırma yapmaktır. Bu sayede kriz anında tanı koyma ve izolasyon için yardım alınacak alanında uzman olmayan personele mobil cihazlar vasıtasıyla karar aşamasında destek sağlanması hedeflenmektedir. Bu amaçla: COVID-19, Normal, Akciğer Opasitesi, Diğer Pnömoni etiketlerine ait 11,293 X-Ray görüntüsünden oluşan veri seti MobileNetV2, NASNetMobile, Xception ve DenseNet121 CNN ağları kullanılarak sınıflandırılmış ve sonuçlar karşılaştırılmıştır. En başarılı sonuçlar DenseNet121 ve MobileNet ağları ile elde edilmiş olup sırası ile %92,16 ve %91,78 doğruluk oranıyla sınıflandırma gerçekleştirilmiştir.

Keywords

Evrişimsel sinir ağları, Koronavirüs, COVID-19, Transfer öğrenimi, X-Ray

Comparison of Lightweight and Traditional CNN Architectures in COVID-19 Detection from Lung X-Ray Images

Abstract

The corona virus epidemic, which affects the respiratory system and causes death in advanced cases, has been going on for about two years Although each country's method of fighting the epidemic is different, the common and valid method is the detection and isolation of the disease. The most critical step for detection and isolation is the correct and fast diagnosis of COVID-19. Virus-specific findings in lung X-ray images shows that these data can be used in the diagnosis of the disease. The aim of the related study is to multi classify by processing X-Ray images of COVID-19 and other lung diseases with machine learning methods. In this way, it is aimed to provide support to the personnel who are not experts in their fields, who will be helped for diagnosis and isolation during the crisis, at the decision stage through mobile devices. For this purpose: The data set consisting of 11,293 X-Ray images of COVID-19, Normal, Lung Opacity, Other Pneumonia labels was classified using the MobileNetV2, NASNetMobile, Xception and DenseNet121 CNN networks and the results were compared. The most successful results were obtained with DenseNet121 and MobileNet networks, and classification was performed with 92.16% and 91.78% accuracy rates, respectively.

Keywords

Convolutional neural network, Coronavirus, Transfer learning, X-Ray

References

• [1] J.-M. Qu, B. Cao, and R.-C. Chen, COVID-19: The Essentials of Prevention and Treatment, 1st ed., Amsterdam, Holland: Elsevier Science, 2020, ch. 1, pp 1-6.
• [2] Z. Allam, Surveying the Covid-19 Pandemic and Its Implications, 1st ed., Amsterdam, Holland: Elsevier Science 2020, ch. 1, pp. 1–7.
• [3] T.-M. Chen, J. Rui, Q.-P. Wang, Z.-Y. Zhao, J.-A. Cui, and L. Yin, “A mathematical model for simulating the phase-based transmissibility of a novel coronavirus,” Infectious Diseases of Poverty, vol. 9, no. 1, pp. 24, 2020.
• [4] World Health Organization. (2021, November 4). Naming the coronavirus disease (COVID-19) and the virus that causes it [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it.
• [5] J. Zheng, “Sars-cov-2: an emerging coronavirus that causes a global threat,” International Journal of Biological Sciences, vol. 16, no. 10, pp. 1678, 2020.
• [6] Karthik, R. Menaka, and H. M, “Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN,” Applied Soft Computing, vol. 99, pp. 106744, 2021.
• [7] M. M. A. Monshi, J. Poon, V. Chung, and F. M. Monshi, “CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR,” Computers in Biology and Medicine, vol. 133, pp. 104375, 2021.
• [8] World Health Orginazation. (2021, August 28). Coronavirus(COVID-19) Dashboard [Online]. Available: http://covid19.who.int.
• [9] L. Wang, Z. Q. Lin, and A. Wong, “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Scientific Reports, vol. 10, no. 1, pp. 19549, 2020.
• [10] Y. Bouchareb et al., “Artificial intelligence-driven assessment of radiological images for COVID-19,” Computers in Biology and Medicine, vol. 136, pp. 104665, 2021.
• [11] N.-A.- Alam, M. Ahsan, M. A. Based, J. Haider, and M. Kowalski, “COVID-19 detection from chest X-ray images using feature fusion and deep learning,” Sensors (Basel), vol. 21, no. 4, pp. 1480, 2021.
• [12] Z. Wang et al., “Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays,” Pattern Recognition, vol. 110, pp. 107613, 2021.
• [13] M. J. Horry et al., “COVID-19 detection through transfer learning using multimodal imaging data,” IEEE Access, vol. 8, pp. 149808–149824, 2020.
• [14] G. D. Rubin et al., “The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the fleischner society,” Radiology, vol. 296, no. 1, pp. 172–180, 2020.
• [15] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Computers in Biology and Medicine, vol. 121, pp. 103792, 2020.
• [16] S. Karakanis and G. Leontidis, “Lightweight deep learning models for detecting COVID-19 from chest X-ray images,” Computers in Biology and Medicine, vol. 130, pp. 104181, 2021.
• [17] D. D. Pukale, S. G. Bhirud and V. D. Katkar, “Content-based image retrieval using deep convolution neural network,” in 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017, pp. 1-5.
• [18] L. L. Ankile, M. F. Heggland, and K. Krange, “Deep convolutional neural networks: A survey of the foundations, selected improvements, and some current applications,” 2020, arXiv:2011.12960.
• [19] D. Arora, M. Garg and M. Gupta, “Diving deep in deep convolutional neural network,” in 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2020, pp. 749-751.
• [20] C. Ouchicha, O. Ammor, and M. Meknassi, “CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images,” Chaos Solitons Fractals, vol. 140, pp. 110245, 2020.
• [21] N. Aloysius and M. Geetha, “A review on deep convolutional neural networks,” in 2017 International Conference on Communication and Signal Processing (ICCSP), 2017, pp. 0588-0592.
• [22] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510-4520.
• [23] E. E.-D. Hemdan, M. A. Shouman, and M. E. Karar, “COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images,” 2020, arXiv:2003.11055.
• [24] U. Seidaliyeva, D. Akhmetov, L. Ilipbayeva, and E. T. Matson, “Real-time and accurate drone detection in a video with a static background,” Sensors (Basel), vol. 20, no. 14, pp. 3856, 2020.
• [25] P. Nagrath, R. Jain, A. Madan, R. Arora, P. Kataria, J. Hemanth, “SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2,” Sustainable Cities and Society, vol. 66, pp. 102692, 2021.
• [26] A. E. Ba Alawi and A. M. Qasem, “Lightweight CNN-based models for masked face recognition,” in 2021 International Congress of Advanced Technology and Engineering (ICOTEN), 2021, pp. 1-5.
• [27] Herdian, G. Putra, and Suharjito, “Classification of C2C e-commerce product images using deep learning algorithm,” International Journal of Advanced Compututer Science Applications, vol. 10, no. 9, 2019, pp. 196-203.
• [28] S.-H. Wang and Y.-D. Zhang, “DenseNet-201-based deep neural network with composite learning factor and precomputation for multiple sclerosis classification,” ACM Transactions on Multimedia Computing, Communications, and Applications., vol. 16, no. 2s, pp. 1–19, 2020.
• [29] F. Chollet, “Xception: Deep Learning with depthwise separable convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1800-1807.
• [30] Rismiyati, S. N. Endah, Khadijah and I. N. Shiddiq, “Xception architecture transfer learning for garbage classification,” in 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), 2020, pp. 1-4.
• [31] P. Bhardwaj and A. Kaur, “A novel and efficient deep learning approach for COVID ‐19 detection using X‐ray imaging modality,” International Journal of Imaging Systems and Technology, vol. 31, no. 4, pp. 1775-1791, 2021.
• [32] K. Srinivasan et al., “Performance comparison of deep CNN models for detecting driver’s distraction,” Computers, Materials & Continua, vol. 68, no. 3, pp. 4109–4124, 2021.
• [33] C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A survey on deep transfer learning,” 2018, arXiv:1808.01974.
• [34] F. Zhuang et al., “A comprehensive survey on transfer learning,” Proceedings of the IEEE, vol. 109, no. 1, pp. 43–76, 2021.
• [35] Kaggle. (2021, June 6). COVID-19 Radiology Dataset [Online]. Available: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database.
• [36] A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artifical Intelligence Review, vol. 53, no. 8, pp. 5455–5516, 2020.
• [37] Keras.io. (2021, August 29). Keras Applications [Online]. Available: https://keras.io/api/applications/.