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Multiclass Brain Tumor Detection with Attention-Embedded CNN Framework: Advancing Toward Decentralized Deep

Anjana Bharati Subba, Arun Kumar Sunaniya, Amrit Mukherjee

    IEEE Journal of Biomedical and Health Informatics
    |November 27, 2025
    PubMed
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    A novel deep learning model accurately detects brain tumors using decentralized learning. This computer-aided diagnosis (CAD) system achieves high accuracy, aiding in early detection and clinical decision-making for life-threatening conditions.

    Area of Science:

    • Medical imaging analysis
    • Artificial intelligence in healthcare
    • Computational neuroscience

    Background:

    • Brain tumor diagnosis relies heavily on radiologist expertise.
    • Accurate and prompt identification of brain tumors is critical for effective treatment.
    • Integrating heterogeneous medical datasets for automated diagnosis presents challenges.

    Purpose of the Study:

    • To develop and evaluate a decentralized learning-based computer-aided diagnosis (CAD) system for brain tumor classification.
    • To improve the accuracy and efficiency of brain tumor detection using deep learning.
    • To address data security and integration challenges in medical AI.

    Main Methods:

    • A custom Convolutional Neural Network (CNN) incorporating inception blocks, attention mechanisms, and residual connections was designed.

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  • The model was trained and validated on a diverse online dataset of MR images categorized into glioma, meningioma, pituitary tumor, and non-tumor classes.
  • Performance was assessed using standard testing accuracy, 10-fold cross-validation, and a federated learning setup.

    • Main Results:

    • The proposed custom CNN achieved a maximum testing accuracy of 98.70%.
    • 10-fold cross-validation resulted in an average testing accuracy of 98.23%.
    • In a federated learning setup, the model attained a testing accuracy of 98.33%.

    Conclusions:

    • The developed deep learning architecture demonstrates high effectiveness in accurately detecting and classifying brain tumors.
    • The decentralized learning approach enhances the integration of medical data for robust diagnostic models.
    • This system shows significant potential as a supportive tool for clinical decision-making in neuro-oncology.