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High-resolution computed tomography (HRCT) scans have become an essential
tool for the diagnosis of lung diseases, especially during the COVID-19 pandemic.
However, the manual analysis of these scans by clinicians can be time-consuming and
error-prone, leading to delayed diagnosis and treatment. In this thesis, we present a deep
learning-based system for the automated estimation of lung damage through the detection
of ground-glass opacities (GGOs) using 3D reconstructed HRCT scans. The system utilizes
a MobileNetV3 backbone combined with a Lite Reduced Atrous Spatial Pyramid Pooling
(LR-ASPP) segmentation head to accurately segment GGO regions in the lung. The 3D
reconstruction of the scans helps to provide clinicians with a more comprehensive view of
the lungs, allowing for better identification and analysis of GGOs.
To train and evaluate our system, we utilized a custom dataset of HRCT scans. The
results demonstrate that our system achieved high accuracy in detecting and segmenting
GGO regions in the lungs, with an overall IOU of 0.62. Additionally, our system was able
to provide clinicians with a more efficient method for analyzing HRCT scans, reducing the
time required for diagnosis and allowing for earlier detection of lung diseases.
In conclusion, our deep learning-based system provides a promising approach for the
automated estimation of lung damage through GGO detection using 3D reconstructed
HRCT scans. By combining state-of-the-art techniques in deep learning and medical
imaging, our system can provide clinicians with an accurate and efficient method for
analyzing HRCT scans, potentially leading to improved patient outcomes and reducing the
burden on healthcare systems.