Adaptable Intelligent Models for Pulmonary Tuberculosis Detection and Classification

Abstract

Tuberculosis is a serious threat to humankind. Every year millions of people are dying as it remains challenging to public health & care. The diversity of healthcare epidemiological settings has aggravated the situation. Third-world countries apply conventional methods to diagnose TB. They take a long time to give a result. Mainly blood, culture, sputum, and biopsies are examined. WHO has marked TB as serious cause of the death among 10 others top life threatening issues globally. The shortage of better healthcare services has worsened the situation in rural India. Recent advancements in Medical sciences have proven significant success in controlling this contagious disease after framed with artificial intelligence. There is a need for cost-effective screening and diagnosis at the initial stage of TB. To hold active TB cases demands new diagnostic approaches. The AI advancement during the last 10 years has been reviewed and analyzed in this paper.