Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet

Authors

  • Adhitio Satyo Bayangkari Karno Universitas Gunadarma
  • Dodi Arif STMIK Jakarta STI&K
  • Indra Sari Kusuma Wardhana STMIK Jakarta STI&K
  • Eka Sally Moreta STMIK Jakarta STI&K

DOI:

https://doi.org/10.31599/abbs9m42

Keywords:

Inception Resnet V2, Convolution Neral Network, Deep Learning, COVID-19, Chest-Xray

Abstract

The availability of medical aids in adequate quantities is very much needed to assist the work of  the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Inception Resnet Version 2 architecture was used in this study to train a dataset of 4000 images, consisting of 4 classifications namely covid, normal, lung opacity and viral pneumonia with 1,000 images each. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 4000 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (97%), Normal (99%) and Viral pneumonia (99%).

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Published

2024-03-26

Issue

Vol. 2 No. 1 (2021): Juni 2021

Section

Artikel

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How to Cite

Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet. (2024). Journal of Informatic and Information Security, 2(1), 57-66. https://doi.org/10.31599/abbs9m42