000			01509nam a22001817a 4500
003			NUST
040			_a0
082			_a005.1,SHE
100			_aShehryar, Muhammad _9114304
245			_aWeb Based Application to Detect Tuberculosis using CXR / _cGC Muhammad Shehryar, GC Alamgir Hasni, GC Mudassir Ahmed, GC Zeeshan Saqib
264			_aMCS, NUST _bRawalpindi _c 2023
300			_a50 p
505			_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.
650			_aUG BESE _9114271
690			_aBESE-25 _9114270
700			_aSupervisor Dr. Nauman Ali _9114305
942			_2ddc _cPR
999			_c595669 _d595669