Automatic Tuberculosis and COVID-19 cough classification using deep learning
Madhurananda Pahar(University of Sheffield), Thomas Niesler(Stellenbosch University), Robin M. Warren(South African Tuberculosis Vaccine Initiative), Andreas H. Diacon(Task Applied Science), Marisa Klopper(South African Medical Research Council), Byron W P Reeve(South African Medical Research Council), Grant Theron(South African Medical Research Council)
2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)
July 20, 2022
Cited by 25
Related Papers
Evolution and expansion of the Mycobacterium tuberculosis PE and PPE multigene families and their association with the duplication of the ESAT-6 (esx) gene cluster regions.
|BMC Evolutionary Biology|2006|428
COVID-19 cough classification using machine learning and global smartphone recordings
|Computers in Biology and Medicine|2021|296
Mycobactericidal Activity of Sutezolid (PNU-100480) in Sputum (EBA) and Blood (WBA) of Patients with Pulmonary Tuberculosis
|PLoS ONE|2014|165
Evaluation of the airway microbiome in nontuberculous mycobacteria disease
|European Respiratory Journal|2018|116
COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features
|Computers in Biology and Medicine|2021|102