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Vo. 6, No. 3, August 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

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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Author Biographies

Hilmy Bahy Hakim, Brawijaya University

Faculty of Computer Science

Agung Setia Budi, Brawijaya University

Faculty of Computer Science

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