000			02340nam a22001577a 4500
082			_a629.8
100			_aNaeem, Abdul Hanan _9119926
245			_aLung damage estimation through ground glass opacity detection from 3D reconstructed HRCT scans / _cAbdul Hanan Naeem
264			_aIslamabad : _bSMME- NUST; _c2023.
300			_a55p. _bIslamabad : SMME- NUST; Soft Copy _c30cm
500			_aHigh-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.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Muhammad jawad khan _9119689
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/32763
942			_2ddc _cTHE
999			_c607436 _d607436