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All Journal Jurnal Penelitian Fisika dan Aplikasinya (JPFA)
Qolby Sabrina, Qolby
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Astronomy Earth & Planetary Sciences Education Materials Science & Nanotechnology Physics

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Identification of COVID-19 Based on Features Texture Histogram and Gray Level Co-Occurrence Matrix (GLCM) Using K-Means Clustering Methods in Chest X-Ray Digital Images Sumarti, Heni; Sabrina, Qolby; Triana, Devi; Septiani, Fahira; Rahmani, Tara Puri Ducha
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol. 13 No. 1 (2023)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v13n1.p51-66

Abstract

Since the last five years of the COVID-19 outbreak, radiological images, such as CT-Scan and Chest X-Ray (CXR), have become essential in diagnosing this disease. However, limited access to facilities such as CT-Scanners and RT-PCR makes CXR images the primary method for COVID-19 testing. This research aims to improve the accuracy of CXR images in identifying COVID-19 patients based on the texture features: histogram and Gray Level Co-occurrence Matrix (GLCM), using the K-Means Clustering method. This study utilized 150 CXR images, including 75 COVID-19 patients confirmed by RT-PCR tests, and 75 patients with negative cases. The method used were consisted of pre-processing, and texture feature extraction with the seven most influential attributes based on gained information (histogram: standard deviation, entropy, skewness, kurtosis, and GLCM: correlation, energy, homogeneity), as well as classification using K-Means clustering methods. The results showed that the classification’s accuracy, sensitivity, and specification are 92%, 91%, and 93%, respectively. This image processing technique is a promising as well as a complementary tool in diagnosing COVID-19 cases, based on CXR images with lower costs and more reliable results.
Co-Authors Nurhalis Majid, Nurhalis Rahmani, Tara Puri Ducha Septiani, Fahira Sumarti, Heni titik - lestariningsih, titik - Triana, Devi