Automated Severity Analysis of Tuberculosis using Particle Swarm Optimization

Tuberculosis (TB) is a major global health problem and is an infectious disease caused by the bacillus mycobacterium tuberculosis, which typically affects the lungs. It spreads through the air when people with active TB cough, sneeze, or otherwise expel infectious bacteria. The mortality rates are high when left undiagnosed and untreated. Unfortunately, diagnosing TB is still a major challenge. The definitive test for TB is the identification of mycobacterium tuberculosis in a clinical sputum or pus sample, which is the current standard. It may take several months to identify this slow-growing organism in the laboratory. Another technique is sputum smear microscopy, in which bacteria in sputum samples are observed under a microscope. In addition, several skin tests based on immune response are available for determining whether an individual has contracted TB. However, skin tests are not always reliable. To overcome this, the proposed system has been developed and it uses computer aided methods for analyzing the severity of the TB. This method allows the segmentation of the chest X-ray image with accuracy using Particle Swarm Optimization (PSO). It also reduces the time for analysis of the disease. The severity of the tuberculosis is displayed at the end of the process.