Lung damage estimation through ground glass opacity detection from 3D reconstructed HRCT scans / (Record no. 607436)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02340nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Naeem, Abdul Hanan |
| 245 ## - TITLE STATEMENT | |
| Title | Lung damage estimation through ground glass opacity detection from 3D reconstructed HRCT scans / |
| Statement of responsibility, etc. | Abdul Hanan Naeem |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 55p. |
| Other physical details | Islamabad : SMME- NUST; Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | High-resolution computed tomography (HRCT) scans have become an essential<br/>tool for the diagnosis of lung diseases, especially during the COVID-19 pandemic.<br/>However, the manual analysis of these scans by clinicians can be time-consuming and<br/>error-prone, leading to delayed diagnosis and treatment. In this thesis, we present a deep<br/>learning-based system for the automated estimation of lung damage through the detection<br/>of ground-glass opacities (GGOs) using 3D reconstructed HRCT scans. The system utilizes<br/>a MobileNetV3 backbone combined with a Lite Reduced Atrous Spatial Pyramid Pooling<br/>(LR-ASPP) segmentation head to accurately segment GGO regions in the lung. The 3D<br/>reconstruction of the scans helps to provide clinicians with a more comprehensive view of<br/>the lungs, allowing for better identification and analysis of GGOs.<br/>To train and evaluate our system, we utilized a custom dataset of HRCT scans. The<br/>results demonstrate that our system achieved high accuracy in detecting and segmenting<br/>GGO regions in the lungs, with an overall IOU of 0.62. Additionally, our system was able<br/>to provide clinicians with a more efficient method for analyzing HRCT scans, reducing the<br/>time required for diagnosis and allowing for earlier detection of lung diseases.<br/>In conclusion, our deep learning-based system provides a promising approach for the<br/>automated estimation of lung damage through GGO detection using 3D reconstructed<br/>HRCT scans. By combining state-of-the-art techniques in deep learning and medical<br/>imaging, our system can provide clinicians with an accurate and efficient method for<br/>analyzing HRCT scans, potentially leading to improved patient outcomes and reducing the<br/>burden on healthcare systems. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Muhammad jawad khan |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/32763">http://10.250.8.41:8080/xmlui/handle/123456789/32763</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 01/18/2024 | 629.8 | SMME-TH-843 | Thesis |