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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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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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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
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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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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.
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Diagnosing Pulmonary Tuberculosis with the Xpert MTB/RIF Test
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Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining

Muhammad Owais1, Muhammad Arsalan1, Tahir Mahmood1

  • 1Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea.

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Summary

This study introduces a new computer-aided diagnosis (CAD) framework for tuberculosis (TB) detection using chest X-rays (CXRs). The system provides descriptive insights from patient data, improving diagnostic accuracy and aiding radiologists.

Keywords:
chest radiographclassification-based retrievalcomputer-aided diagnosislung diseaseneural networktuberculosis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Tuberculosis (TB) is a fatal infectious disease requiring early diagnosis.
  • Computer-aided diagnosis (CAD) tools for TB from chest radiographs (CXRs) exist but lack descriptive information.
  • Existing methods offer binary classification (TB positive/negative) without detailed insights.

Purpose of the Study:

  • To develop a comprehensive CAD framework for effective TB diagnosis.
  • To provide visual and descriptive information from a patient database.
  • To enhance diagnostic decision-making for radiologists.

Main Methods:

  • A fusion-based deep classification network was proposed for CAD decisions.
  • A multilevel similarity measure algorithm using multiscale information fusion was devised.
  • The framework retrieves best-matched cases from a previous patient database.

Main Results:

  • The framework was evaluated on two prominent CXR datasets.
  • The classification model achieved high performance metrics (e.g., 0.965 AUC).
  • The proposed system outperformed various state-of-the-art methods.

Conclusions:

  • A comprehensive CAD framework for TB diagnosis from CXRs was presented.
  • The framework retrieves relevant cases and clinical observations, aiding radiologists.
  • Retrieval results support radiologists in making effective diagnostic decisions and subjective validation.