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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Journal of Computer Science and Informatics Engineering (J-Cosine)
Ditha Nurcahya Avianty
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Computer Science & IT

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Ekstraksi Fitur Citra Radiografi Thorax Menggunakan DWT dan Moment Invariant: Feature Extraction of Thorax Radiography Image Using DWT and Moment Invariant I Gede Pasek Suta Wijaya; Ditha Nurcahya Avianty; Fitri Bimantoro; Rina Lestari
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 5 No 2 (2021): December 2021
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v5i2.423

Abstract

COVID-19 is an infectious disease caused by the coronavirus family, 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 confirming the positive status of a COVID-19 patient requires further examination. Lung examination using chest X-ray images taken through X-rays of COVID-19 patients can be one way to confirm the patient's condition before/after the rapid test. This paper proposes a feature extraction model to detect COVID-19 through chest radiography using a combination of Discrete Wavelet Transform (DWT) and Moment Invariant features. In this case, haar wavelet transform and seven Hu moments were used to extract image features in order to find unique features that represent chest radiographic images as suspected COVID-19, pneumonia, or normal. To find out the uniqueness of the proposed features, it is coupled with the kNN and generic ANN classification techniques. Based on the performance parameters assessed, it turns out that the wavelet-based and moment invariant thorax radiographic image feature model can be used as a unique feature associated with three categories: Normal, Pneumonia, and Covid-19. This is indicated by the accuracy value of 82.7% in the kNN classification technique and the accuracy, precision, and recall of 86%, 87%, and 86% respectively with the ANN classification technique.
The Comparison of SVM and ANN Classifier for COVID-19 Prediction Ditha Nurcahya Avianty; Prof. I Gede Pasek Suta Wijaya; Fitri Bimantoro
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 2 (2022): Vol. 13, No. 2 August 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i02.p06

Abstract

Coronavirus 2 (SARS-CoV-2) is the cause of an acute respiratory infectious disease that can cause death, popularly known as Covid-19. Several methods have been used to detect COVID-19-positive patients, such as rapid antigen and PCR. Another method as an alternative to confirming a positive patient for COVID-19 is through a lung examination using a chest X-ray image. Our previous research used the ANN method to distinguish COVID-19 suspect, pneumonia, or expected by using a Haar filter on Discrete Wavelet Transform (DWT) combined with seven Hu Moment Invariants. This work adopted the ANN method's feature sets for the Support Vector Machine (SVM), which aim to find the best SVM model appropriate for DWT and Hu moment-based features. Both approaches demonstrate promising results, but the SVM approach has slightly better results. The SVM's performances improve accuracy to 87.84% compared to the ANN approach with 86% accuracy.
Co-Authors Fitri Bimantoro I Gede Pasek Suta Wijaya Prof. I Gede Pasek Suta Wijaya Rina Lestari