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Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:
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Pulmonary Tuberculosis IV01:26

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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.
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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.
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A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial Intelligence

Akansha Nayyar1, Rahul Shrivastava1, Shruti Jain2

  • 1Department of BT & BI, JUIT, Solan, H.P., India.

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Machine learning models accurately identify tuberculosis (TB) from chest X-rays (CXRs). Support Vector Machine (SVM) achieved 93.5% accuracy, showing AI

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Chest X-ray imagesEdge features .Gray Level Difference StatisticsMachine learning techniquesMycobacterium tuberculosisShape features

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Tuberculosis (TB) is a global health issue requiring early detection.
  • Diagnosing TB from chest X-rays (CXRs) can be challenging for radiologists.
  • Artificial Intelligence (AI) offers new possibilities for automated CXR analysis.

Purpose of the Study:

  • To develop and evaluate Machine Learning (ML) models for automated TB detection from CXRs.
  • To compare the performance of various classifiers including SVM, logistic regression, decision tree, kNN, and ANN.
  • To advance TB diagnosis through AI, potentially leading to earlier treatment and better patient outcomes.

Main Methods:

  • Utilized distinct parameters like edge, shape, and Gray Level Difference Statistics (GLDS).
  • Employed dataset splitting ratios of 70:30 and 80:20 for model training and validation.
  • Assessed multiple ML classifiers for automated tuberculosis identification.

Main Results:

  • Achieved 93.5% accuracy using Support Vector Machine (SVM) with a linear kernel and a 70:30 data split.
  • Compared performance against different feature extraction techniques, dataset splits, and existing studies.
  • Evaluated hybrid parameters for enhanced classification accuracy.

Conclusions:

  • AI demonstrates significant potential for improving tuberculosis detection rates.
  • The study compared various ML models, including SVM, decision tree, kNN, ANN, and logistic regression.
  • AI-driven TB detection can lead to earlier diagnosis and improved disease management.