Abstract: During the COVID-19 pandemic, the precise evaluation of lung impairments using computed tomography (CT) scans became critical for understanding and managing the disease; however, specialists faced a high workload and the urgent need to deliver fast and accurate results. To address this, deep learning models offered a promising solution by automating lung identification and lesion localization associated with COVID-19. This study employs the semantic segmentation technique Mask R-CNN, integrated with a ResNet-50 backbone, to analyze CT scans of COVID-19 patients. The model was trained using an annotated dataset, enhancing its ability to accurately segment and delineate the lung parenchyma in CT images. The results showed that Mask R-CNN achieved a Dice Similarity Coefficient (DSC) of 93.4%, demonstrating high concordance between the segmented areas and clinically relevant regions. These findings highlight the effectiveness of the proposed approach for precise lung tissue segmentation in CT scans, enabling quantitative assessments of lung impairments and providing valuable insights for diagnosis and patient monitoring.

Wilmer Alberto Pacheco Llacho, Eveling Castro-Gutierrez and Luis David Huallpa Tapia, “Lung Parenchyma Segmentation Using Mask R-CNN in COVID-19 Chest CT Scans” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160214

@article{Llacho2025,
title = {Lung Parenchyma Segmentation Using Mask R-CNN in COVID-19 Chest CT Scans},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160214},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160214},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Wilmer Alberto Pacheco Llacho and Eveling Castro-Gutierrez and Luis David Huallpa Tapia}
}