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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Classification of Systems-II01:31

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Related Experiment Videos

Federated autoencoder-based clinical decision framework with hybrid class balancing.

Manjula Rani Indupalli1, G Pradeepini2

  • 1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522 502, Andhra Pradesh, India. indupalli.manjula@gmail.com.

Scientific Reports
|June 5, 2026
PubMed
Summary

A new federated learning model enhances disease classification accuracy to 92.5% while preserving patient data privacy. This privacy-preserving AI approach addresses security concerns in healthcare by enabling collaborative training without sharing sensitive medical records.

Keywords:
AutoencoderClass imbalanceData securityDeep learningDisease classificationFederated learningGenerative augmentationHealthcare AIModel convergencePrivacy-preserving AI

Related Experiment Videos

Area of Science:

  • Artificial Intelligence in Healthcare
  • Medical Informatics
  • Machine Learning for Diagnostics

Background:

  • Deep learning improves disease classification but raises significant patient data privacy and security concerns.
  • Conventional centralized models risk data leaks and non-compliance with regulations like GDPR and HIPAA.
  • Scalability and security are critical challenges for AI in distributed healthcare systems.

Purpose of the Study:

  • To present a privacy-preserving federated learning architecture for collaborative medical model training.
  • To enhance disease classification accuracy while ensuring patient data confidentiality.
  • To address data imbalance and improve model robustness in diverse healthcare datasets.

Main Methods:

  • Implemented a federated learning architecture for secure, distributed model training without raw data exposure.
  • Utilized autoencoder-driven hierarchical feature extraction for improved classification and minimal information loss.
  • Integrated a hybrid class-balancing mechanism with generative augmentation and Synthetic Minority Over-Sampling Technique (SMOTE) for unbalanced datasets.
  • Employed adaptive federated averaging for strong convergence on non-IID distributed medical data.

Main Results:

  • Achieved 92.5% classification accuracy, outperforming traditional CNN (87.2%) and LSTM (89.1%) models.
  • Demonstrated strong convergence and robustness even with non-IID distributed medical data.
  • The model proved resistant to adversarial attacks, enhancing practical security.
  • Successfully addressed class imbalance, improving sensitivity for minority disease classes.

Conclusions:

  • The proposed federated learning model offers a scalable, secure, and privacy-preserving foundation for AI-driven healthcare diagnostics.
  • This approach bridges the gap between high-accuracy disease categorization and privacy-preserving artificial intelligence.
  • The study validates the potential of federated medical AI to transform diagnostics while maintaining regulatory compliance and data security.