01437nam a22001457a 4500003000500000082001000005100002000015245011000035264003900145300002900184520092400213650005601137700004101193856005701234NUST  a629.8  a Munir, Zonaira  aDiagnosing and localizing Covid-19 in High resolution CT(HRCT) scans using Deep learning /cZonaira Munir  aIslamabad : bSMME- NUST; c2023.   a62p. ;bSoft Copyc30cm.  aWith the break-out of covid-19 as a world-wide pandemic that has a higher spread rate,
it became a need to find a solution that would work in the favor of the patient as well
as the radiologist. Since 2020, there have been many attempts to cater for the problem.
Many researchers proposed detection and classification models in an attempt to
automate some parts of the diagnostics process.
The common methods found in the reported literature includes using models like
VGG16, FCNN, Unet, ResUnet, Inception net and Alex net for the tasks of detection
and classification of covid-19 benign or malignant.
This thesis aims to explore the possibility of detecting and localizing covid-19. The
covid lesions were segmented and then detected using Attention Res-Unet. The lungs
were segmented into the major lobes using Unet and then an attempt was made to
localize the detected lesions with respect to segmented Lung Lobes.   aMS Robotics and Intelligent Machine Engineering      aSupervisor : Dr. Muhammad jawad khan  uhttp://10.250.8.41:8080/xmlui/handle/123456789/34963