Automated detection of Mycobacterium tuberculosis using transfer learning | Zendy

Abdullahi Umar Ibrahim | Zendy; Emrah Güler | Zendy; Meryem Güvenir | Zendy; Kaya Süer | Zendy; Sertan Serte | Zendy; Mehmet Özsöz | Zendy

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Automated detection of Mycobacterium tuberculosis using transfer learning

Author(s) -

Abdullahi Umar Ibrahim,

Emrah Güler,

Meryem Güvenir,

Kaya Süer,

Sertan Serte,

Mehmet Özsöz

Publication year - 2021

Publication title -

the journal of infection in developing countries

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.322

H-Index - 49

eISSN - 2036-6590

pISSN - 1972-2680

DOI - 10.3855/jidc.13532

Subject(s) - mycobacterium tuberculosis , tuberculosis , artificial intelligence , transfer of learning , sensitivity (control systems) , computer science , mycobacterium tuberculosis complex , machine learning , pattern recognition (psychology) , medicine , pathology , electronic engineering , engineering

Quantitative analysis of Mycobacterium tuberculosis using microscope is very critical for diagnosing tuberculosis diseases. Microbiologist encounter several challenges which can lead to misdiagnosis. However, there are 3 main challenges: (1) The size of Mycobacterium tuberculosis is very small and difficult to identify as a result of low contrast background, heterogenous shape, irregular appearance and faint boundaries (2) Mycobacterium tuberculosis overlapped with each other making it difficult to conduct accurate diagnosis (3) Large amount of slide can be time consuming and tedious to microbiologist and which can lead to misinterpretations.

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