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

Authors

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

DOI:

https://doi.org/10.31599/jiforty.v2i1.646

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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How to Cite

Satyo, A. S. B. K., Arif, D. ., Kusuma Wardhana, I. S. ., & Moreta, E. S. . (2021). Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet. Journal of Informatic and Information Security, 2(1). https://doi.org/10.31599/jiforty.v2i1.646

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