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Related Experiment Video

Updated: Jun 26, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Classification of distinct lung diseases using novel enhanced long short-term memory based optimization methodology.

A Sundar Raj1, P Anand Raj2, E Dinesh3

  • 1Department of Biomedical Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. sundarraj.a@egspec.org.

Scientific Reports
|June 24, 2026
PubMed
Summary

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This study introduces an ELSTM-AZOA framework for improved chest X-ray lung disease classification. The novel approach enhances accuracy and precision for diagnosing conditions like pneumonia and lung cancer.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Chest X-ray (CXR) classification is vital for early lung disease diagnosis.
  • Challenges include low image quality, similar disease features, and unstable classification.
  • Existing methods require improvement for reliable computer-aided diagnosis.

Purpose of the Study:

  • To propose a novel ELSTM-AZOA framework for multiclass lung disease classification using CXR images.
  • To enhance the accuracy and reliability of computer-aided lung disease diagnosis.
  • To classify six categories: healthy lung, tuberculosis, pneumonia, lung cancer, COPD, and COVID-19.

Main Methods:

  • Preprocessing CXR images using balance contrast enhancement.
  • Lung region segmentation with U-Net++.
Keywords:
American zebra optimization algorithmBalance contrast enhancement techniqueEnhanced long short-term memoryGLCM featuresLung disease classificationNIH CXR datasetStatistical featuresU-Net +  +

Related Experiment Videos

Last Updated: Jun 26, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

  • Feature extraction via statistical and gray level co-occurrence matrix methods.
  • Classification using an enhanced long short-term memory (ELSTM) network.
  • Model parameter optimization with the American zebra optimization algorithm (AZOA).

    • Main Results:

    • The ELSTM-AZOA framework achieved superior performance compared to existing methods.
    • Demonstrated a 6.36% increase in accuracy and a 6.43% increase in precision.
    • Successfully classified six distinct lung conditions from CXR images.

    Conclusions:

    • The proposed ELSTM-AZOA framework offers a robust and reliable method for computer-aided lung disease classification.
    • The findings highlight the potential of the framework for improving clinical decision-making.
    • This approach shows promise for enhancing diagnostic accuracy in medical imaging analysis.