Feature Extraction With Forest Classifer To Predicate Covid 19 Based On Thorax X-Ray Results

Ali Mustopa; Hendri Mahmud Nawawi; Sarifah Agustiani; Siti Khotimatul Wildah

doi:10.32520/stmsi.v11i2.1966

Feature Extraction With Forest Classifer To Predicate Covid 19 Based On Thorax X-Ray Results

Ali Mustopa, Hendri Mahmud Nawawi, Sarifah Agustiani, Siti Khotimatul Wildah

Abstract

Coronavirus 19 (COVID-19) is a highly contagious infection caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a new virus for which no cure has been found, marked by the increasing death rate worldwide. Coronavirus disease which can cause pneumonia which attacks the air sacs of the lungs with symptoms of dry cough, sore throat to acute respiratory distress (ARDS) that occurs in COVID-19 patients. One of the ways to detect the virus is by detecting chest X-rays in the patient. Over the past decade's mechine learning technology has developed rapidly and is integrated into CAD systems to provide accurate accuracy. This research was conducted by detecting thoracic radiographs using feature extraction Hu-Moments, Harralic and Histogram and detecting the best accuracy with a classification algorithm to detect the results of COVID-19. The study was conducted by testing the dataset obtained from the Kaggle repository which has images, namely 1281 X-rays of COVID-19, 3270 X-rays Normal, 1656 X-rays of pneumonia, and X-rays of bacteria-pneumonia 3001. In general, this research is included in the Good category because it produces the highest accuracy by the Random forest classification algorithm where the accuracy result is 84% and the standard deviation is 0.015847. In addition, the research also produced Kappa of 0.713. The results of this accuracy are carried out in several stages, namely by feature extraction in the form of hu-moments, Harralic and histogram. In this study, the best results were given by the Random forest algorithm with feature extraction Histogram and Hu-Moment.

Full Text:

PDF

References

M. A. Shereen, S. Khan, A. Kazmi, N. Bashir, and R. Siddique, “COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses,” J. Adv. Res., vol. 24, pp. 91–98, 2020, doi: 10.1016/j.jare.2020.03.005.

L. Yang et al., “Epidemiological and clinical features of 200 hospitalized patients with corona virus disease 2019 outside Wuhan, China: A descriptive study.,” J. Clin. Virol., vol. 129, no. March, p. 104475, 2020, doi: 10.1016/j.jcv.2020.104475.

H. A. Rothan and S. N. Byrareddy, “The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak,” Journal of Autoimmunity. 2020, doi: 10.1016/j.jaut.2020.102433.

A. Torres and C. Cillóniz, Clinical management of bacterial pneumonia. 2015.

C. C. Lai, T. P. Shih, W. C. Ko, H. J. Tang, and P. R. Hsueh, “Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges,” Int. J. Antimicrob. Agents, vol. 55, no. 3, p. 105924, 2020, doi: 10.1016/j.ijantimicag.2020.105924.

N. E. M. Khalifa, M. H. N. Taha, A. E. Hassanien, and S. Elghamrawy, “Detection of coronavirus (COVID-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest x-ray dataset,” arXiv. 2020.

R. Munawarah, O. Soesanto, and M. R. Faisal, “Penerapan Metode Support Vector Machine,” Kumpul. J. Ilmu Komput., 2016.

B. Sugara and A. Subekti, “Penerapan Support Vector Machine (Svm) Pada Small Dataset Untuk Deteksi Dini Gangguan Autisme,” J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 177–182, 2019, doi: 10.33480/pilar.v15i2.649.

A. Hosny, C. Parmar, J. Quackenbush, L. H. Schwartz, and H. J. W. L. Aerts, “Artificial intelligence in radiology,” Nature Reviews Cancer. 2018, doi: 10.1038/s41568-018-0016-5.

R. S. Bahri and I. Maliki, “PERBANDINGAN ALGORITMA TEMPLATE MATCHING DAN FEATURE EXTRACTION PADA OPTICAL CHARACTER RECOGNITION,” J. Komput. dan Inform. Edisi. I Vol. 1, Maret 2012, 2012.

A. I. Khan, J. L. Shah, and M. M. Bhat, “CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images,” Comput. Methods Programs Biomed., 2020, doi: 10.1016/j.cmpb.2020.105581.

U. Sait, “Curated Chest X-Ray Image Dataset for COVID-19,” Kaggle Repository, 2020. https://www.kaggle.com/unaissait/curated-chest-xray-image-dataset-for-covid19.

M. Ghozali and H. Sumarti, “Deteksi Tepi pada Citra Rontgen Penyakit COVID-19 Menggunakan Metode Sobel,” J. Imejing Diagnostik, vol. 6, no. 2, pp. 51–59, 2020, doi: 10.31983/jimed.v6i2.5840.

N. Nafisah, R. I. Adam, and Carudin, “Klasifikasi K-NN dalam Identifikasi Penyakit COVID-19 Menggunakan Ekstraksi Fitur GLCM,” J. Appl. Informatics Comput., vol. 5, no. 2, pp. 128–132, 2021.

S. Y. E. Simarmata, Y. A. Sari, and S. Adinugroho, “Klasifikasi Citra Makanan Menggunakan Algoritme Learning Vector Quantization Berdasarkan Ekstraksi Fitur Color Histogram dan Gray Level Co-occurrence Matrix,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2369–2378, 2019.

DOI: https://doi.org/10.32520/stmsi.v11i2.1966

Article Metrics

Abstract view : 396 times
PDF - 223 times

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Username
Password
Remember me