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p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Computer Science and Humanitarian AI
Zaki Ambadar
Biomedical Engineering, Institut Teknologi Sepuluh Nopember
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Two-Dimensional Segmentation to Reconstruct Three-Dimensional Covid-19 Patient’s Lung CT Using Active Contour Zaki Ambadar; Tri Arief Sardjono; Nada Fitrieyatul Hikmah
International Journal of Computer Science and Humanitarian AI Vol. 2 No. 1 (2025): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v2i1.12417

Abstract

Beginning in December 2019, SArS-CoV-2, also referred to as COVID-19, quickly spread over the world. With two recurrent waves and a 3.3% fatality rate, COVID-19 has caused over 4 million cases in Indonesia. RT-PCR, antigen, and RT-LAMP are currently the main techniques for COVID-19 detection and diagnosis. A CT scan is usually used for additional diagnosis when RT-PCR results are uncertain, but extra confirmation is required. The need to inform patients about the effects of COVID-19 on the lungs is increasing as the number of cases of the virus keeps rising and diagnosis and first aid techniques advance. The severity of COVID-19-induced pneumonia, which shows up as ground-glass opacities (GGO), which are gray patches in the lung cavity, may be seen on a single-slice CT scan. The degree of lung injury can be measured using image processing techniques. In this study, two- and three-dimensional representations of the lungs were created utilizing a multi-slice CT scan and image processing techniques like active contour and marching cubes. The suggested approach produced an average volume difference of 5% and an accuracy of 62% based on intersection over union (IoU).
Co-Authors Nada Fitrieyatul Hikmah Tri Arief Sardjono