Background: Tuberculosis (TB) has become a global pandemic, and its eradication requires
efficient screening methods, diagnostic tests, and effective drugs. Artificial intelligencebased
Computer-aided Diagnostic (CADx) systems are purported to play a significant role in the
mass screening of TB.
Discussion: The research on the development of CADx systems started four decades ago, and a
large number of CADx systems have been developed till date. However, no independent survey
focussing on the advancements in these systems has been presented. This paper fills this gap by
consolidating the advancements and presents a comprehensive survey of CADx systems for TB detection
developed till date with a focus on their underlying principles. It also discusses a practical
model using which CADx systems can be used for screening TB in places where medical facilities
and experts are not adequately available.
Conclusion: The paper also presents an overview of the current state of deep learning-based CADx
systems. The development of these systems will remain in focus in the near future and will improve
state-of-the-art performance in various medical domains.
Keywords: Computer-aided diagnosis, CAD systems, tuberculosis, medical imaging, chest X-rays, chest radiographs, machine
learning, deep learning.
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