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All Journal Journal of Applied Science and Advanced Engineering
Yoeni, Indrasary
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Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering Physics

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Journal : Journal of Applied Science and Advanced Engineering

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Prediction of Covid-19 Disease Using X-Ray Images with Deep Learning Algorithm Ikawati, Vidya; Yoeni, Indrasary; Prihatmanto, Ary Setijadi
Journal of Applied Science and Advanced Engineering Vol. 1 No. 1 (2023): JASAE: March 2023
Publisher : Master Program in Mechanical Engineering, Gunadarma University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59097/jasae.v1i1.11

Abstract

The capacity of Indonesian medical personnel, especially pulmonary and radiology specialists, is still far from the proportionate ratio of Indonesia's population. This limitation is one of Indonesia's main issues in realizing adequate health services for lung sufferers. Furthermore, the diagnosis process is one of the keys to obtaining appropriate and fast treatment procedures for sufferers. This paper will review the research conducted by the PPTIK ITB team in developing a tool for diagnosing lung disease with the help of Deep Learning. In this study, deep learning models play a role in classifying diseases based on an X-Ray image of the lungs. At this stage, the performance of three deep learning models, ResNet50, ResNet101, and VGG19, will be compared in classifying COVID-19, Pneumonia, and tuberculosis. The performance metrics to be compared include accuracy, precision, recall, and F1 score. The test results show that, on average, the VGG19 model gives the best results on the four performance metrics compared to the other two models.
Co-Authors Ary Setijadi Prihatmanto Vidya Ikawati, Vidya