Detection of COVID’19 through low resolution CT scan images using Deep Learning / (Record no. 609134)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02215nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Arif, Hajra |
| 245 ## - TITLE STATEMENT | |
| Title | Detection of COVID’19 through low resolution CT scan images using Deep Learning / |
| Statement of responsibility, etc. | Hajra Arif |
| 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 | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 51p |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Coronavirus emerged as a deadly disease in 2019 killing almost 6.25 million people, with<br/>its variants still being discovered. For timely medical treatment it is necessary to<br/>accurately and rapidly diagnose the disease. The main test used for diagnosis was “The<br/>Reverse Transcription Polymerase Chain Reaction (RT-PCR)” but due to limited<br/>availability of RT-PCR equipment at the time of outbreak, alternative methods were used<br/>to mitigate the damage. One of them was the computed tomography (CT) scans, a noninvasive imaging approach. Utilizing this CT data, deep learning (DL) models were<br/>developed to expedite the diagnostic procedure. Due to privacy concerns, the original CT<br/>scans were not shared with the public which caused hindrance in the research and<br/>development of accurate DL methods. Hence, datasets were made from secondary<br/>sources, either by extracting images from preprints or saving images in any other format<br/>than DICOM, which generated low resolution images. To address this issue, preprocessing techniques were applied to generate better results of the DL models. The pixel<br/>intensities in images are normalized such that they lie in the range of the actual values of<br/>a CT scan in the Hounsfield unit scale and then given as an input to the model. Diagnostic<br/>performance was assessed by F1-score (84%), AUC (94%) and Accuracy (81%), which<br/>is better than the performance achieved without pre-processing. This study proves that<br/>enhancing the image quality, through pre-processing techniques, can improve the results<br/>when good quality data is unavailable and accurate models can be made for detecting<br/>any disease at the time of the outbreak. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Omer Gilani |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/30539">http://10.250.8.41:8080/xmlui/handle/123456789/30539</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 | 05/15/2024 | 610 | SMME-TH-724 | Thesis |