Model Design of The Image Recognition of Lung CT Scan for COVID-19 Detection Using Artificial Neural Network

Maulana Akbar Dwijaya (Telkom University)
Umar Ali Ahmad (Telkom University)
Rudi Purwo Wijayanto (PUSPIPTEK)
Ratna Astuti Nugrahaeni (Telkom University)

JURNAL NASIONAL TEKNIK ELEKTRO
Vol 11, No 1: March 2022

COVID-19 has become a pandemic and is a big problem that needs to be checked out immediately. CT scan images can explain the lung conditions of COVID-19 patients and have the potential to be a clinical diagnostic tool. In this research, we classify COVID-19 by recognizing images on a computer tomography scan (CT scan) of the lungs using digital image processing and GLCM feature extraction techniques to obtain grayscale level values in CT images, followed by the creation of an artificial neural network model. So that the model can classify CT scan images, the results in this research obtained the most optimal model for COVID-19 classification performance with 90% accuracy, 88% precision, 91% recall, and 90% F1 score. This research can be a useful tool for clinical practitioners and radiologists to assist them in the diagnosis, quantification, and follow-up of COVID-19 cases.