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Project Name

 AI for Medical Device: Mycobacterium Tuberculosis Identification Using CNNs


Project Intro


Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis (M. tuberculosis). It is a global infectious disease problem, especially prevalent in undeveloped and developing countries. According to the WHO 2017 report, it is one of the top ten causes of death worldwide and currently the most contagious disease.

Conventional light microscopy is the most widely used detection method. Pathologists check for the presence of M. tuberculosis through the acid-fast stained sputum smear. The disadvantage of this method is that the process requires a lot of manpower, and the accuracy is only about 50% to 70%. Therefore, for disease prevention, it would be very beneficial to have an automatic, fast and accurate M. tuberculosis identification system. In this project, artificial intelligence (AI) deep learning technology is applied to develop an automated M. tuberculosis identification system. Simultaneously, deep learning optimization methods will also be improved and optimized in order for medical exam speed and accuracy of TB to be effectively increased. Academic contribution will come from the innovation of the deep learning optimization improvements in the study. Relevant results can then be extended to the identification of nontuberculous Mycobacteriosis (NTM) and extrapulmonary TB. All of the aforementioned can provide thorough assistance for tuberculosis control. Not only does this project provide a great extent of AI technical education, the results could also easily be transferred to the industry, which may ultimately increase the competitiveness of AI medical devices of Taiwan.


Project Host


Principal Investigator 

Yung-Nien Sun, Distinguished Professor



Co-Principal Investigator

Ming-Huwi Horng, Professor

Dept. CS, NPU


Nan-Haw Chow, Professor

Dept. Pathology

College of Medicine, NCKU


Website To Link

Lab: NCKU CSIE Visual System Lab


Host's Public Contact Method


E-mail:This email address is being protected from spambots. You need JavaScript enabled to view it.


Representative Pictures Of The Research







70101 台南市東區大學路1號 成功校區 資訊工程系 舊館1樓4220辦公室
No.4220, CSIE Dept (old building), No.1, University Road, Tainan City 701, Taiwan (R.O.C)

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