Automated detection of Mycobacterium tuberculosis using transfer learning

  • Abdullahi Umar Ibrahim Department of Biomedical Engineering, Near East University, Nicosia, Turkey
  • 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 Department of Electrical Electronics Engineering, Near East University, Nicosia, Turkey
  • Mehmet Ozsoz Department of Biomedical Engineering, Near East University, Nicosia, Turkey
Keywords: Mycobacterium tuberculosis, Pretrained-AlexNet, CNN, Transfer Learning, Diagnosis

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.

Methodology: To solve these challenges and limitations, we proposed an automated-based detection method using pretrained AlexNet to trained the model in 3 sets of experiments A, B and C and adjust the protocols accordingly. We compared the detection of tuberculosis using AlexNet Models with the ground truth result provided by microbiologist and analyzed inconsistencies between network models and human.

Results: 98.15 % accuracy, 96.77% sensitivity and 100% specificity for experiment A, 98.09% accuracy, 98.59% sensitivity and 97.67% specificity for experiment B and 98.73% testing accuracy, 98.59 sensitivity, 98.84% specificity ofr experiment C which sound robust and promising.

Conclusions: The results indicated that network performance was successful with high accuracies, sensitivities and specificities and it can be used to support microbiologist for diagnosis of tuberculosis.

Published
2021-05-31
How to Cite
1.
Ibrahim AU, Guler E, Guvenir M, Suer K, Serte S, Ozsoz M (2021) Automated detection of Mycobacterium tuberculosis using transfer learning. J Infect Dev Ctries 15:678-686. doi: 10.3855/jidc.13532
Section
Original Articles