Proceedings of the 2nd Global Health and Innovation in conjunction with 6th ORL Head and Neck Oncology Conference (ORLHN 2021)

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COVID-19 Prediction Based on DWT and Moment Invariant Features of Radiography Image Using the Artificial Neural Network Classifier

Authors
Ditha Nurcahya Avianty1, I Gede Pasek Suta Wijaya1, *, Fitri Bimantoro1, Rina Lestari2, Triana Dyah Cahyawati2
1Department Informatics Engineering, University of Mataram, Indonesia
2Faculty of Medicine, University of Mataram, Indonesia
*Corresponding author. Email: gpsutawijaya@unram.ac.id
Corresponding Author
I Gede Pasek Suta Wijaya
Available Online 21 February 2022.
DOI
10.2991/ahsr.k.220206.030How to use a DOI?
Keywords
COVID-19; Radiographic Image; Artificial Neural Network; Discrete Wavelet Transform; Moment Invariant
Abstract

COVID-19 is an infectious disease caused by a family of coronaviruses, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The fastest method to identify the presence of this virus is a rapid antibody or antigen test, but to confirm the positive status of a COVID-19 patient, further examination is recommended. Lung examination using chest radiography images taken through X-rays of COVID-19 patients can be one of the method to confirm the patient’s condition before/after the rapid test. In this paper, a model to detect COVID-19 through chest radiography images is proposed by using a combination of Discrete Wavelet Transform (DWT) and Moment Invariant features, and the Artificial Neural Network (ANN) classifiers. In this case, the haar wavelet transform and seven Hu moments were used to extracting the image’s features. The main aim of the work is to find the best features and ANN model for predicting chest radiography images as COVID-19 suspect, pneumonia, or normal. The k-fold cross-validation test on the best parameters obtained accuracy up to 86.32%, a precision level of 86.35%, and a recall rate of 86.26%.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2nd Global Health and Innovation in conjunction with 6th ORL Head and Neck Oncology Conference (ORLHN 2021)
Series
Advances in Health Sciences Research
Publication Date
21 February 2022
ISBN
10.2991/ahsr.k.220206.030
ISSN
2468-5739
DOI
10.2991/ahsr.k.220206.030How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

risenwbib
TY  - CONF
AU  - Ditha Nurcahya Avianty
AU  - I Gede Pasek Suta Wijaya
AU  - Fitri Bimantoro
AU  - Rina Lestari
AU  - Triana Dyah Cahyawati
PY  - 2022
DA  - 2022/02/21
TI  - COVID-19 Prediction Based on DWT and Moment Invariant Features of Radiography Image Using the Artificial Neural Network Classifier
BT  - Proceedings of the 2nd Global Health and Innovation in conjunction with 6th ORL Head and Neck Oncology Conference (ORLHN 2021)
PB  - Atlantis Press
SP  - 152
EP  - 162
SN  - 2468-5739
UR  - https://doi.org/10.2991/ahsr.k.220206.030
DO  - 10.2991/ahsr.k.220206.030
ID  - Avianty2022
ER  -