Web Based Application to Detect Tuberculosis using CXR / (Record no. 595669)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01509nam a22001817a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | 0 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.1,SHE |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Shehryar, Muhammad |
| 245 ## - TITLE STATEMENT | |
| Title | Web Based Application to Detect Tuberculosis using CXR / |
| Statement of responsibility, etc. | GC Muhammad Shehryar, GC Alamgir Hasni, GC Mudassir Ahmed, GC Zeeshan Saqib |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | MCS, NUST |
| Name of producer, publisher, distributor, manufacturer | Rawalpindi |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 50 p |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Tuberculosis is a very infectious respiratory disease and is currently the leading cause of mortality worldwide, ranking higher than both malaria and HIV/AIDS. As a result, it is vital to promptly diagnose TB to limit its transmission, enhance preventative measures, and reduce the mortality rate associated with the disease. Various procedures and tools have been employed to diagnose TB early, practically all of which needed a visit to the doctor and were not available to the public. This work presents an automated and accurate approach for diagnosing TB that may be used by the general population and does not require special imaging equipment or conditions. An application will be developed for the detection of TB using CXRs and deep learning techniques. The application will use a convolutional neural network (CNN) to classify CXRs as normal or indicative of TB. The CNN will be trained on dataset of annotated CXRs to learn the relevant features for TB detection. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | UG BESE |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) | |
| Topical term or geographic name as entry element | BESE-25 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor Dr. Nauman Ali |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Project Report |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| Military College of Signals (MCS) | Military College of Signals (MCS) | General Stacks | 08/30/2023 | 005.1,SHE | MCSPCS-456 | Project Report |