Automated detection of Mycobacterium tuberculosis using transfer learning.

Journal: Journal of infection in developing countries
PMID: 34106892

Abstract

INTRODUCTION: 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.

Authors

  • Abdullahi Umar Ibrahim
    Department of Biomedical Engineering, Near East University, Nicosia, Turkey. abdullahi.umaribrahim@neu.edu.tr.
  • Emrah Guler
    Department of Medical Microbiology and Clinical Microbiology, Near East University, Nicosia, Turkey.
  • Meryem Guvenir
    Department of Medical Microbiology and Clinical Microbiology, Near East University, Nicosia, Turkey.
  • Kaya Suer
    Department of Infectious Disease and Clinical Microbiology, Near East University, Nicosia, Turkey.
  • Sertan Serte
    Electrical and Electronic Engineering, Near East University, Nicosia, North Cyprus via Mersin 10, Turkey. Electronic address: http://www.neu.edu.tr.
  • Mehmet Ozsoz
    Department of Biomedical Engineering, Near East University, Nicosia, Turkey.

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