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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, 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, 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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Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using

Chutinun Prasitpuriprecha1, Sirima Suvarnakuta Jantama1, Thanawadee Preeprem1

  • 1Department of Biopharmacy, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand.

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Summary

This study introduces a deep learning system for detecting tuberculosis (TB) and drug-resistant TB (DR-TB) from chest X-rays. The developed TB-DRC-DSS achieved high accuracy, outperforming existing methods in classifying TB strains.

Keywords:
chest X-raydrug-resistantensemble deep learningmulticlass-AMIStuberculosisweb application diagnosis

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Tuberculosis (TB) diagnosis and drug resistance classification remain critical global health challenges.
  • Current diagnostic methods can be time-consuming and may lack precision in identifying drug-resistant strains.
  • The need for rapid, accurate, and accessible diagnostic tools for TB and its resistant forms is paramount.

Purpose of the Study:

  • To develop a decision support system (TB-DRC-DSS) for detecting tuberculosis and categorizing drug-resistant strains using deep learning.
  • To create an ensemble model integrating multiple Convolutional Neural Network (CNN) architectures for enhanced diagnostic accuracy.
  • To provide a web-based platform for real-time TB and drug resistance classification.

Main Methods:

  • Development of a deep learning ensemble model using EfficientNetB7, MobileNetV2, and Dense-Net121 architectures.
  • Application of image segmentation, data augmentation, and decision fusion techniques to optimize classification performance.
  • Training and validation on a combined dataset of 7,008 chest X-ray images from multiple public sources.
  • Implementation of a web application for user interaction and diagnosis.

Main Results:

  • The TB-DRC-DSS demonstrated significant improvements in classifying drug-sensitive TB (DS-TB) against drug-resistant TB (DR-TB) by an average of 43.3%.
  • Enhanced accuracy in differentiating between DS-TB and MDR-TB (28.1%), DS-TB and XDR-TB (6.2%), and MDR-TB and XDR-TB (9.4%).
  • The multiclass model achieved a high accuracy of 92.6% on the test dataset and 92.8% on a random subset, with a user preference score of 9.52/10 from medical staff.

Conclusions:

  • The proposed deep learning ensemble model offers a highly accurate and efficient solution for TB and DR-TB detection and classification.
  • The web-based TB-DRC-DSS provides a valuable tool for clinicians, improving diagnostic capabilities and potentially patient outcomes.
  • The system's performance and positive user feedback indicate its potential for widespread clinical adoption.