Diagnosing and localizing Covid-19 in High resolution CT(HRCT) scans using Deep learning / (Record no. 607320)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01528nam a22001697a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Munir, Zonaira |
| 245 ## - TITLE STATEMENT | |
| Title | Diagnosing and localizing Covid-19 in High resolution CT(HRCT) scans using Deep learning / |
| Statement of responsibility, etc. | Zonaira Munir |
| 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 | 62p. ; |
| Other physical details | Soft Copy |
| Dimensions | 30cm. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | With the break-out of covid-19 as a world-wide pandemic that has a higher spread rate,<br/>it became a need to find a solution that would work in the favor of the patient as well<br/>as the radiologist. Since 2020, there have been many attempts to cater for the problem.<br/>Many researchers proposed detection and classification models in an attempt to<br/>automate some parts of the diagnostics process.<br/>The common methods found in the reported literature includes using models like<br/>VGG16, FCNN, Unet, ResUnet, Inception net and Alex net for the tasks of detection<br/>and classification of covid-19 benign or malignant.<br/>This thesis aims to explore the possibility of detecting and localizing covid-19. The<br/>covid lesions were segmented and then detected using Attention Res-Unet. The lungs<br/>were segmented into the major lobes using Unet and then an attempt was made to<br/>localize the detected lesions with respect to segmented Lung Lobes. |
| 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/123456789/34963">http://10.250.8.41:8080/xmlui/handle/123456789/34963</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 | 12/12/2023 | 629.8 | SMME-TH-880 | Thesis |