Automatic Tuberculosis Screening Using Chest Radiographs
United States National Library of Medicine · National Center for Biotechnology Information · +7 more institutions
Abstract
Tuberculosis is a major health threat in many regions of the world. Opportunistic infections in immunocompromised HIV/AIDS patients and multi-drug-resistant bacterial strains have exacerbated the problem, while diagnosing tuberculosis still remains a challenge. When left undiagnosed and thus untreated, mortality rates of patients with tuberculosis are high. Standard diagnostics still rely on methods developed in the last century. They are slow and often unreliable. In an effort to reduce the burden of the disease, this paper presents our automated approach for detecting tuberculosis in conventional posteroanterior chest radiographs. We first extract the lung region using a graph cut segmentation method. For…
Citation impact
- FWCI
- 11.13
- Percentile
- 100%
- References
- 61
Authors
14- SJStefan JaegerCorresponding
United States National Library of Medicine, National Center for Biotechnology Information
- AKAlexandros Karargyris
United States National Library of Medicine, National Center for Biotechnology Information
- SCSema Candemir
United States National Library of Medicine, National Center for Biotechnology Information
- LFLes Folio
National Institutes of Health, National Institutes of Health Clinical Center
- JSJenifer Siegelman
Brigham and Women's Hospital, Harvard University
Topics & keywords
- Tuberculosis
- Medicine
- Artificial intelligence
- Classifier (UML)
- Radiography
- Computer science
- Radiology
- Pathology