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Related Concept Videos

Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

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Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
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Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
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Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
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Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
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Automated detection of Mycobacterium tuberculosis using transfer learning.

Abdullahi Umar Ibrahim1, Emrah Guler2, Meryem Guvenir2

  • 1Department of Biomedical Engineering, Near East University, Nicosia, Turkey. abdullahi.umaribrahim@neu.edu.tr.

Journal of Infection in Developing Countries
|June 9, 2021
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Summary

This study introduces an automated detection method for Mycobacterium tuberculosis, overcoming challenges in microscopic diagnosis. The AI model achieved high accuracy, supporting microbiologists in diagnosing tuberculosis.

Keywords:
CNNDiagnosisMycobacterium tuberculosisPretrained-AlexNetTransfer Learning

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Area of Science:

  • Medical diagnostics
  • Computer vision
  • Microbiology

Background:

  • Microscopic analysis of Mycobacterium tuberculosis is crucial for tuberculosis diagnosis.
  • Challenges include small bacterial size, low contrast, overlapping cells, and time-consuming manual review, leading to potential misdiagnosis.

Purpose of the Study:

  • To develop and evaluate an automated detection method for Mycobacterium tuberculosis using a pretrained AlexNet model.
  • To address limitations in manual microscopic diagnosis and improve diagnostic accuracy.

Main Methods:

  • An automated detection system was developed using a pretrained AlexNet convolutional neural network.
  • The model was trained and tested across three experimental sets (A, B, and C) with adjusted protocols.
  • Performance was compared against ground truth results provided by microbiologists.

Main Results:

  • Experiment A: 98.15% accuracy, 96.77% sensitivity, 100% specificity.
  • Experiment B: 98.09% accuracy, 98.59% sensitivity, 97.67% specificity.
  • Experiment C: 98.73% accuracy, 98.59% sensitivity, 98.84% specificity.

Conclusions:

  • The automated AlexNet-based detection method demonstrates robust and promising performance.
  • The system achieved high accuracy, sensitivity, and specificity, indicating its potential to aid microbiologists in tuberculosis diagnosis.