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Vo. 6, No. 3, August 2021

Issue Published : Aug 31, 2021
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Early Detection of COVID-19 Patient’s Survavibility Based On The Image Of Lung X-Ray Image Using Deep Neural Networks

https://doi.org/10.22219/kinetik.v6i3.1265
Hilmy Bahy Hakim
Brawijaya University
Fitri Utaminingrum
Br awijaya Univers ity
Agung Setia Budi
Brawijaya University

Corresponding Author(s) : Fitri Utaminingrum

f3_ningrum@ub.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vo. 6, No. 3, August 2021
Article Published : Aug 31, 2021

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Abstract

SARS-CoV-2 causes an infection called COVID-19, which is caused by a new coronavirus. One of the symptomps that dangerous to the patients is developing pneumonia in their lungs. To detect pneumonia symptoms, one of the newest methods is using CNN (Convolution Neural Networks). The problem is when able to detect pneumonia, the patient's survivability, which knowing this will be helpful to decide the priority for each patient, is still in question. The CNN used in this research to classify the patient’s future condition, but met some major problems that the dataset is very few and unbalance. The image augmentation was used to multiply the dataset, and class weight was applied to prevent miscalculation on minority class. 6 CNN architectures used to find the best model. The result VGG19 architecture has the best overall accuracy, in training, it has 80% accuracy, 89% accuracy invalidation, and 82% f1 score accuracy on classifying the testing dataset means the best model if looking for accuracy on prediction, but this cost a prediction time that longest compared to other CNN architectures. MobileNet is the fastest, but it cost much worse on prediction accuracy, only 55%. The ResNet50 model has balanced prediction accuracy/time, it got 77% f1 accuracy, and also 8.49 seconds of prediction time, 9 seconds less than VGG19.

Keywords

Covid-19 Lung X-ray Image Processing CNN Deep Neural Networks
Bahy Hakim, H. ., Utaminingrum, F., & Setia Budi, A. . (2021). Early Detection of COVID-19 Patient’s Survavibility Based On The Image Of Lung X-Ray Image Using Deep Neural Networks. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(3). https://doi.org/10.22219/kinetik.v6i3.1265
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References
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Read More

References


W. Zhang, “Imaging changes of severe COVID-19 pneumonia in advanced stage,” Intensive Care Med., vol. 46, no. 5, pp. 841–843, 2020. https://doi.org/10.1007/s00134-020-05990-y

H. A. Sidharta, F. Filipi, R. A. Jaskandi, and F. Nugroho, “Design and Implementation LETS (Low Power Cluster Server) for Sustaining UMKM during Pandemic,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, pp. 77–82, 2021. https://doi.org/10.22219/kinetik.v6i1.1162

Y. Xu et al., “Characteristics of pediatric SARS-CoV-2 infection and potential evidence for persistent fecal viral shedding,” Nat. Med., vol. 26, no. 4, pp. 502–505, 2020. https://doi.org/10.1038/s41591-020-0817-4

C. Huang et al., “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” Lancet, vol. 395, no. 10223, pp. 497–506, 2020. https://doi.org/10.1016/S0140-6736(20)30183-5

H. Sharma, J. S. Jain, P. Bansal, and S. Gupta, “Feature extraction and classification of chest X-ray images using CNN to detect pneumonia,” Proc. Conflu. 2020 - 10th Int. Conf. Cloud Comput. Data Sci. Eng., pp. 227–231, 2020. https://doi.org/10.1109/Confluence47617.2020.9057809

M. Heidari, S. Mirniaharikandehei, A. Z. Khuzani, G. Danala, Y. Qiu, and B. Zheng, “Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms,” Int. J. Med. Inform., vol. 144, no. September, p. 104284, 2020. https://doi.org/10.1016/j.ijmedinf.2020.104284

M. M. Eid and Y. H. Elawady, “Efficient Pneumonia Detection for Chest Radiography Using ResNet-Based SVM,” Eur. J. Electr. Eng. Comput. Sci., vol. 5, no. 1, pp. 1–8, 2021. https://doi.org/10.24018/ejece.2021.5.1.268

P. Quah, A. Li, J. Phua, and J. Phua, “Mortality rates of patients with COVID-19 in the intensive care unit: A systematic review of the emerging literature,” Crit. Care, vol. 24, no. 1, pp. 1–4, 2020. https://doi.org/10.1186/s13054-020-03006-1

Bachir, “COVID-19 chest xray,” COVID-19 chest xray, 2020.

K. Pasupa and W. Sunhem, “A comparison between shallow and deep architecture classifiers on small dataset,” Proc. 2016 8th Int. Conf. Inf. Technol. Electr. Eng. Empower. Technol. Better Futur. ICITEE 2016, 2017. https://doi.org/10.1109/ICITEED.2016.7863293

Kaladharanalytics, “Covid-19-X-ray-Image-Augmentation-,” 2020.

T. Devries and G. W. Taylor, “Dataset Augmentation In Feature Space,” pp. 1–12, 2017.

J. Terstriep, “Keras Spatial,” 2019. https://doi.org/10.1145/3356471.3365240

B. Usage, “The NumPy Array: A Structure for Efficient Numerical Computation,” pp. 22–30. https://doi.org/10.1109/MCSE.2011.37

A. Nurhopipah and N. A. Larasati, “CNN hyperparameter optimization using random grid coarse-to-fine search for face classification,” vol. 4, 2021. https://doi.org/10.22219/kinetik.v6i1.1185

G. Licea, “Towards supporting Software Engineering using Deep Learning : A case of Software Requirements Classification,” 2017. https://doi.org/10.1109/CONISOFT.2017.00021

R. T. Puteri and F. Utaminingrum, “Micro-sleep detection using combination of haar cascade and convolutional neural network,” ACM Int. Conf. Proceeding Ser., pp. 130–135, 2020. https://doi.org/10.1145/3427423.3427433

H. Bi, F. Xu, Z. Wei, Y. Xue, and Z. Xu, “An Active Deep Learning Approach for Minimally Supervised PolSAR Image Classification,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 11, pp. 9378–9395, 2019. https://doi.org/10.1109/TGRS.2019.2926434

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.

M. Yamazaki et al., “Yet Another Accelerated SGD: ResNet-50 Training on ImageNet in 74.7 seconds,” arXiv, 2019.

A. G. Howard and W. Wang, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2012.

X. Qi and T. Wang, “Comparison of Support Vector Machine and Softmax Classifiers in Computer Vision,” pp. 151–155, 2017. https://doi.org/10.1109/ICMCCE.2017.49

X. Guo, Y. Yin, C. Dong, G. Yang, and G. Zhou, “On the Class Imbalance Problem *,” pp. 192–201, 2008. https://doi.org/10.1109/ICNC.2008.871

M. I. N. Zhu et al., “Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data,” vol. 3536, no. c, 2018. https://doi.org/10.1109/ACCESS.2018.2789428

“Estimate class weights for unbalanced datasets.”

J. Xu, Z. Zhang, T. Friedman, Y. Liang, and G. van den Broeck, “A semantic loss function for deep learning with symbolic knowledge,” arXiv, 2017.

A. Yulianto, P. Sukarno, and N. A. Suwastika, “Improving AdaBoost-based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset,” J. Phys. Conf. Ser., vol. 1192, no. 1, 2019. https://doi.org/10.1088/1742-6596/1192/1/012018

D. Morales, E. Talavera, and B. Remeseiro, “Playing to distraction: towards a robust training of CNN classifiers through visual explanation techniques,” 2020.

Author Biographies

Hilmy Bahy Hakim, Brawijaya University

Faculty of Computer Science

Agung Setia Budi, Brawijaya University

Faculty of Computer Science

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