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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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Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm.

Ramya Mohan1, Seifedine Kadry2,3,4, Venkatesan Rajinikanth1

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This summary is machine-generated.

This study introduces an automated system for detecting Tuberculosis (TB) using chest X-rays. The VGG19 model achieved 98.6% accuracy, aiding in early disease diagnosis and management.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Infectious Disease Diagnostics

Background:

  • Communicable diseases, including Tuberculosis (TB), are increasing globally.
  • Early detection of TB is crucial for reducing disease spread and improving patient outcomes.
  • Chest X-rays are a primary tool for diagnosing pulmonary TB.

Purpose of the Study:

  • To develop an automated system for Tuberculosis detection using chest X-ray images.
  • To assist pulmonologists in disease severity assessment and treatment planning.
  • To enhance the accuracy and efficiency of TB diagnosis.

Main Methods:

  • Utilized a pre-trained VGG19 model for feature extraction.
  • Implemented image pre-processing, deep feature mining, and handcrafted feature extraction.
  • Optimized features using the Seagull Algorithm and serial concatenation.
  • Performed binary classification with 10-fold cross-validation using SVM-Medium Gaussian classifier in MATLAB®.

Main Results:

  • Achieved a high classification accuracy of 98.6190% for TB detection.
  • Demonstrated the effectiveness of combining deep and handcrafted features.
  • Validated the system's performance through rigorous cross-validation.

Conclusions:

  • The developed automated system shows significant potential for accurate and efficient TB detection.
  • The VGG19-based approach with optimized feature concatenation offers a promising tool for clinical decision support.
  • This technology can aid in the timely management of Tuberculosis, contributing to public health efforts.