The advancement and well-developed technologies in global medical field, tuberculosis remains a major health problem. To solve the problem of tuberculosis, artificial intelligence (AI) provides the way for solving problem in real world and enlightens the world in bringing a human's brain to a machine. This paper aims to detect the presence of mycobacterium tuberculosis infection within a short span of time compared to ancient technique. It is designed in such a way that the breath of the infected person can be used to diagnose the disease at premier stage. The main objective is to design and implement a portable diagnostic kit for Tuberculosis using Neural Networks and Artificial Intelligence. The tool kit called Electronic Nose which is significant artificial intelligent component constructed through neural network contains a biosensor having an electrode coated with the galectin. The signals of hybridization on binding will be captured and processed by machine learning sensor and the output is displayed using artificial intelligence. Earlier case of diagnosis approached on the basis of grouping microorganisms days together. But this study faster to find the report in an hour. Therefore the time taken for diagnosing the presence of the bacterium can be reduced and this also paves the way for starting the treatment immediately.
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