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Related Experiment Video

Updated: May 28, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Explainable Split-Learning-Based Framework for Accurate Pulmonary Nodule Classification.

Amira Bouamrane1, Makhlouf Derdour2, Ahmed Alksas3

  • 1LIAOA Laboratory, Department of Computer Science, University of Souk Ahras, Souk Ahras 41000, Algeria.

Bioengineering (Basel, Switzerland)
|May 27, 2026
PubMed
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This summary is machine-generated.

This study introduces a privacy-preserving AI for lung cancer detection using collaborative feature extraction. The new explainable approach significantly reduces false negatives and positives in Computed Tomography (CT) scans.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer is the leading cause of cancer death globally.
  • Computer-Aided Diagnosis Systems (CADx) improve pulmonary nodule classification but face data limitations and privacy concerns.
  • Existing CADx systems struggle with false negatives and false positives.

Purpose of the Study:

  • To develop a privacy-preserving, collaborative feature extraction approach for efficient and diverse lung nodule classification.
  • To reduce false positives and false negatives in lung cancer diagnosis using Computed Tomography (CT) scans.
  • To enhance the trustworthiness and generalizability of AI models in medical diagnostics.

Main Methods:

  • A novel explainable feature-based split learning approach was proposed.
Keywords:
CT scanResNet-50XAIdiagnosisexplainabilitylung cancersplit learning

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Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
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Last Updated: May 28, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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Published on: May 19, 2023

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
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Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

  • Utilized a split ResNet-50 architecture for client-side feature extraction.
  • Employed a hybrid 2D-CNN with an attention mechanism on the server-side for classification.
  • Evaluated using ablation studies (ConvNeXt-Tiny, EfficientNetB0) and external datasets.
  • Assessed trustworthiness with Local Interpretable Model-agnostic Explanations (LIME) and Grad-CAM.

    • Main Results:

    • Achieved 99.38% accuracy and F1-score with 1.23% false negatives and 0% false positives on the primary dataset.
    • Demonstrated robustness on unseen datasets, yielding 99.28% accuracy (1.24% FN, 0% FP) on one and 95.74% accuracy (7.07% FN, 1.41% FP) on another.
    • Confirmed data diversity and privacy preservation through evaluations.

    Conclusions:

    • The proposed explainable split learning approach effectively enhances lung nodule classification accuracy and efficiency.
    • The method ensures data privacy and diversity while significantly reducing diagnostic errors.
    • The model exhibits strong generalizability and trustworthiness, making it a promising tool for real-world clinical application.