01397nam a22001457a 4500003000500000040000600005082001400011100002400025245013800049264003400187300000900221505097900230650001201209700003001221NUST  a0  a005.1,SHE  aShehryar, Muhammad   aWeb Based Application to Detect Tuberculosis using CXR /cGC Muhammad Shehryar, GC Alamgir Hasni, GC Mudassir Ahmed, GC Zeeshan Saqib  aMCS, NUST bRawalpindic 2023  a50 p  aTuberculosis 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.  aUG BESE  aSupervisor Dr. Nauman Ali