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Related Experiment Videos

An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray

Nagwa Yaseen Hegazy1, Mohamed S Sawah2,3

  • 1Information Systems Department, Faculty of Information Systems and Computer Science, October 6 University, Giza, 12585, Egypt. Nagwa.Yaseen.Cs@o6u.edu.eg.

Scientific Reports
|March 28, 2026
PubMed
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This study introduces an optimized EfficientNetB0 model for multi-label chest X-ray classification, improving diagnostic accuracy for co-occurring thoracic diseases. The framework offers a robust solution for computer-aided diagnosis, outperforming other models.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Chest radiography is vital for diagnosing thoracic diseases.
  • Interpreting multiple co-occurring pathologies on X-rays presents a significant challenge.
  • Existing deep learning models often oversimplify this complexity.

Purpose of the Study:

  • To develop an optimized deep learning framework for accurate multi-label classification of chest X-rays.
  • To address the challenge of co-occurring pathologies in thoracic disease diagnosis.
  • To improve the performance of computer-aided diagnosis systems.

Main Methods:

  • An optimized EfficientNetB0 framework was developed using the NIH dataset.
  • The model incorporated CLAHE-based contrast enhancement and strategic class balancing.
Keywords:
CLAHE preprocessingChest X-ray classificationEfficientNetB0Thoracic pathology detectionTransfer learning

Related Experiment Videos

  • A comparative transfer learning strategy was employed to maintain multi-label complexity.

    • Main Results:

    • The proposed model achieved a macro-average AUC of 0.906 and recall of 0.824.
    • It outperformed DenseNet121 and MobileNetV2 in diagnostic performance.
    • Strong per-class discrimination was observed, notably for Pneumonia (AUC=0.950) and Cardiomegaly (AUC=0.946).

    Conclusions:

    • The framework effectively balances learning capacity and generalization in multi-label chest X-ray classification.
    • It provides a robust and interpretable solution for detecting co-occurring thoracic pathologies.
    • This approach enhances computer-aided diagnosis for complex clinical scenarios.