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Related Experiment Video

Updated: Jun 27, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

ACross-Paradigm CNN-Swin Transformer Ensemble with Super-Resolution Enhancement for Multi-Class Alzheimer's Disease

Mohamed H Habeb1, Reem A Alnanih2,3, Lamiaa A Elrefaei1,4

  • 1Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt.

Bioengineering (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an advanced AI framework using deep learning and enhanced MRI preprocessing to accurately classify Alzheimer's disease stages. The novel approach achieves high accuracy, improving early diagnosis potential for this global health challenge.

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Neuroscience

Background:

  • Alzheimer's disease (AD) diagnosis is challenging, especially in early stages, with current clinical methods lacking accuracy.
  • Deep learning for MRI-based AD diagnosis faces issues with image quality, generalization, and subtle variations between classes.

Purpose of the Study:

  • To develop a robust, end-to-end brain MRI framework for multi-class classification of Alzheimer's disease stages.
  • To address limitations in current deep learning approaches for AD diagnosis using AI and intelligent healthcare technologies.

Main Methods:

  • Implemented an attention-based ensemble of deep learning models, including Swin Transformer, EfficientNet-B3, and MobileNetV2.
  • Utilized Real-ESRGAN for enhanced image preprocessing to improve 2-D MRI slice quality by mitigating degradation.
Keywords:
Alzheimer’s diagnosisCNN–Swin ensembleMRI classificationimage super-resolutionintelligent healthcare systems engineeringvision transformers

Related Experiment Videos

Last Updated: Jun 27, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

  • Employed Squeeze-and-Excitation attention for feature fusion to capture multi-scale features and global dependencies.

    • Main Results:

    • Achieved high classification accuracy: 94.47% and 92.28% for two proposed framework variations on a public Alzheimer's MRI dataset.
    • Demonstrated robustness and clinical interpretability through comprehensive metrics and qualitative analysis.
    • Established promising benchmark performance on a standardized public dataset.

    Conclusions:

    • The proposed AI framework shows significant potential for accurate, early-stage Alzheimer's disease diagnosis using enhanced MRI analysis.
    • Cross-paradigm deep learning ensembles combined with super-resolution preprocessing offer a promising direction for intelligent healthcare technologies in neurodegenerative disease detection.