Automated Detection of Covid-19 from HRCT Images using Deep Learning / (Record no. 608375)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02046nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Gulzaib, Ujala |
| 245 ## - TITLE STATEMENT | |
| Title | Automated Detection of Covid-19 from HRCT Images using Deep Learning / |
| Statement of responsibility, etc. | Ujala Gulzaib |
| 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 | 72p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Accurate and early diagnosis of coronavirus disease is essential for quick decisionmaking and patient management. Polymerase chain reactions (PCR) confirm Covid-19<br/>but are limited due to prolonged execution time for analysis. Early disease detection is<br/>possible using High Resonance Computerized Tomography (HRCT) images as input in<br/>an automated disease prediction model. This is helpful for patients to take early<br/>precautionary measures even before consulting a radiologist. Different pre-trained deep<br/>neural networks have been used and higher accuracy has been achieved in this model.<br/>This study aims to detect disease more accurately and rapidly by differentiating between<br/>normal and infected images. The dataset available on Kaggle is divided into 70:15:15<br/>ratios in which training, validation, and test dataset contain 18676, 4000, and 4000 images,<br/>respectively. Five different pre-trained deep neural networks ResNet50V2,<br/>MobileNetV2, EffecientNetB0, InceptionV3, and Xception Net are compared using an<br/>open-source data set to train and validate our proposed model. The proposed model<br/>achieves an overall accuracy of 99.9% on ResNet50V2 followed by 99.1% on<br/>MobileNetV2, 98.5% on Xception, 98.4% on Inception, and 94.1% on Efficient NetB0.<br/>Mobile NetV2 achieves the highest covid-19 sensitivity of 99.5% and ResNet50V2<br/>gives 99.9% covid specificity among all other networks. In future, such prediction<br/>models can be used in clinical settings to develop computer-aided diagnosis (CAD)<br/>models for clinicians. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Sciences (BMS) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Amer Sohail Kashif |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/31383">http://10.250.8.41:8080/xmlui/handle/123456789/31383</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 | 02/27/2024 | 610 | SMME-TH-792 | Thesis |