International Journal on Advanced Science, Engineering and Information Technology, Vol. 9 (2019) No. 1, List of accepted papers. , DOI:10.18517/ijaseit.9.1.7567

Artificial Intelligence in Diagnosing Tuberculosis: A Review

Syeda Shaizadi Meraj, Razali Yaakob, Azreen Azman, Siti Nuralain Mohd Rum, Azree Shahrel Ahmad Nazri


Tuberculosis (TB) is among top ten causes of deaths worldwide. It is ranked 2nd only after the HIV/AIDS. In third world countries, the diagnosis of TB is done through conventional methods. To obtain diagnostic results from these methods it takes 1-2 weeks. To lower the detection time and raise the accuracy of diagnosis, Artificial Intelligence (AI), Computer-aided Detection (CADe) and Diagnosis (CADx) methods have been used for the diagnosis of TB. These technologies assist in medical field for diagnosing the diseases through clinical signs and symptoms as well as radiological images of the patient. Advances in AI algorithms, has unveiled great promises in identifying the presence and absence of TB.  As of late, there are many attempts have been made to formulate the strategies to increase the accuracy of TB diagnosis using the AI and machine learning approach. This paper, describes the diverse AI approaches employed in the diagnosis of TB.


Tuberculosis (TB); Classification; Artificial Intelligence (AI); Artificial Neural Network (ANN); Machine Learning (ML); Artificial Intelligence in Medicine (AIM); Convolution Neural Networks (CNNs)

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