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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.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the...
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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, 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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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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Computer-aided diagnosis using embedded ensemble deep learning for multiclass drug-resistant tuberculosis

Kanchana Sethanan1, Rapeepan Pitakaso2, Thanatkij Srichok2

  • 1Department of Industrial Engineer, Faculty of Engineering, Research Unit on System Modelling for Industry, Khon Kaen University, Khon Kaen, Thailand.

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A new web application, TB-DRD-CXR, uses deep learning to categorize tuberculosis patients by drug resistance. It achieves high accuracy and efficiency, outperforming existing methods for drug-sensitive and drug-resistant TB classification.

Keywords:
artificial multiple intelligence systemcomputer aided diagnosisdrug resistantensemble deep learningtuberculosis

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Computational Biology

Background:

  • Tuberculosis (TB) poses a significant global health challenge, with drug resistance complicating treatment and increasing mortality.
  • Accurate classification of TB strains, including drug-sensitive TB (DS-TB) and various forms of drug-resistant TB (DR-TB) like multidrug-resistant TB (MDR-TB), pre-extensively drug-resistant TB (pre-XDR-TB), and extensively drug-resistant TB (XDR-TB), is crucial for effective patient management.
  • Existing diagnostic methods may lack the speed and accuracy required for rapid clinical decision-making.

Purpose of the Study:

  • To develop and evaluate the TB-DRD-CXR web application for classifying TB patients into drug resistance subgroups.
  • To implement an ensemble deep learning model capable of distinguishing between five TB subtypes: DS-TB, DR-TB, MDR-TB, pre-XDR-TB, and XDR-TB.

Main Methods:

  • An ensemble deep learning model was developed, incorporating novel fusion techniques, image segmentation, data augmentation, and learning rate strategies.
  • The model's performance was benchmarked against state-of-the-art techniques and standard Convolutional Neural Network (CNN) architectures.
  • The TB-DRD-CXR application was developed and usability tested with medical staff.

Main Results:

  • The proposed ensemble deep learning model demonstrated superior performance over existing methods, with accuracy increases ranging from 4.0% to 33.9%.
  • Significant accuracy improvements were observed compared to standard CNN models (e.g., DenseNet201, NASNetMobile, EfficientNetV2B3), with gains up to 93.4%.
  • The TB-DRD-CXR application achieved a high accuracy rate of 96.7%, excellent time-based efficiency (4.16 goals/minute), and overall relative efficiency (100%).

Conclusions:

  • The TB-DRD-CXR web application effectively categorizes TB patients based on drug resistance levels using an advanced ensemble deep learning model.
  • The system exhibits high accuracy, efficiency, and user satisfaction (SUS score of 96.7%), indicating its potential for clinical adoption.
  • The developed application represents a significant advancement in TB diagnostics, offering improved performance over current methodologies.