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Related Experiment Videos

CCEO-DCABNet: Chronological Chaotic Evolution Optimization-Enabled Hybrid Deep Learning for Multiclass Disease

Leena Patil1,2, Bindu Garg1, Massimo Donelli3

  • 1Bharati Vidyapeeth (Deemed to be) University, College of Engineering, Pune 411043, India.

Diagnostics (Basel, Switzerland)
|July 15, 2026
PubMed
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This study introduces a novel deep learning model, CCEO-DCABNet, for accurate multiclass lung disease classification from chest X-rays. The federated learning approach ensures data privacy while achieving high diagnostic performance.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Chest X-rays are crucial for diagnosing lung diseases, but classification is challenging due to data heterogeneity, overlapping features, and privacy concerns.
  • Distinguishing between various lung diseases is difficult owing to similar clinical and imaging characteristics.

Purpose of the Study:

  • To propose a novel deep channel-attention broad convolutional neural network (CCEO-DCABNet) for accurate multiclass lung disease classification.
  • To address data privacy challenges using a federated learning (FL) framework.
  • To enhance classification performance through chaotic evolution optimization.

Main Methods:

  • Implemented a federated learning framework for collaborative model training without raw data sharing.
Keywords:
Gaussian filterMMPU-Netchaotic evolution optimizationchest X-ray imagefederated learning

Related Experiment Videos

  • Applied Gaussian filter denoising and multiscale unsharp masking for image preprocessing.
  • Utilized a deep channel-attention broad convolutional neural network (DCABNet) optimized by chaotic evolution optimization (CCEO).

    • Main Results:

    • The CCEO-DCABNet model achieved high performance metrics: 96.98% accuracy, 96.41% true positive rate (TPR), and 97.45% true negative rate (TNR).
    • Demonstrated effective multiclass classification of lung diseases from chest X-ray images.

    Conclusions:

    • The proposed CCEO-DCABNet framework effectively classifies multiple lung diseases while preserving data privacy via federated learning.
    • The model shows superior classification performance, supporting reliable computer-aided diagnosis in clinical settings.