Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study

Fajar Astuti Hermawati, Bambang Riyanto Trilaksono, Anto Satriyo Nugroho, Elly Matul Imah, Lukas, Telly Kamelia, Tati L.E.R. Mengko, Astri Handayani, Stefanus Eric Sugijono, Benny Zulkarnaien, Rahmi Afifi, Dimas Bintang Kusumawardhana

Research output: Contribution to journalArticlepeer-review


This study aims to automatically analyze and extract abnormalities in the lung field due to Coronavirus Disease 2019 (COVID-19). Types of abnormalities that can be detected are Ground Glass Opacity (GGO) and consolidation. The proposed method can also identify the location of the abnormality in the lung field, that is, the central and peripheral lung area. The location and type of these abnormalities affect the severity and confidence level of a patient suffering from COVID-19. The detection results using the proposed method are compared with the results of manual detection by radiologists. From the experimental results, the proposed system can provide an average error of 0.059 for the severity score and 0.069 for the confidence level. This method has been implemented in a web-based application for general users. • A method to detect the appearance of viral pneumonia imaging features, namely Ground Glass Opacity (GGO) and consolidation on the chest Computed Tomography (CT) scan images. • This method can separate the lung field to the right lung and the left lung, and it also can identify the detected imaging feature's location in the central or peripheral of the lung field. • Severity level and confidence level of the patient's suffering are measured.

Original languageEnglish
Article number102507
Publication statusPublished - Jun 2024


  • Consolidation
  • Ground-Glass-Opacity
  • Image segmentation
  • Image thresholding
  • Severity level


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