Transfer deep learning and explainable AI framework for brain tumor and Alzheimer's detection across multiple datasets

Topic: Amyloid, tau and blood-test diagnosis

The model achieves accuracy rates of 94% on the brain tumor dataset, 81% on the augmented Alzheimer dataset, and 93% on the third dataset, underscoring its capability to differentiate various neurological conditions. Furthermore, the integration of SHapley Additive exPlanations (SHAP) provides a transparent view of the model's decision-making process, allowing clinicians to understand which regions of the MRI scans contribute to the classification outcomes. This research demonstrates the potential of combining advanced deep learning techniques with explainability to improve diagnostic accuracy and trust in AI applications within healthcare.

Key findings

• The model achieves accuracy rates of 94% on the brain tumor dataset, 81% on the augmented Alzheimer dataset, and 93% on the third dataset, underscoring its capability to differentiate various neurological conditions.
• Furthermore, the integration of SHapley Additive exPlanations (SHAP) provides a transparent view of the model's decision-making process, allowing clinicians to understand which regions of the MRI scans contribute to the classification outcomes.
• This research demonstrates the potential of combining advanced deep learning techniques with explainability to improve diagnostic accuracy and trust in AI applications within healthcare.

Why it matters

A deep learning model classified brain tumor and Alzheimer's MRI scans with reported accuracy of 94%, 81%, and 93% across datasets, and used SHAP to show which scan regions drove its decisions. It is a diagnostic tool study, not a treatment or cure, and the design is not stated.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.3389/fmed.2025.1618550
• PubMed: 40612575
• Original source →
• Free full text: Frontiers in Medicine →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{alsubai2025-40612575,
  author  = {Alsubai, Shtwai and Ojo, Stephen and Nathaniel, Thomas I. and Ayari, Mohamed Arselene and Baili, Jamel and Almadhor, Ahmad S. and Hejaili, Abdullah Al},
  title   = {{Transfer deep learning and explainable AI framework for brain tumor and Alzheimer's detection across multiple datasets}},
  journal = {Frontiers in Medicine},
  year    = {2025},
  doi     = {10.3389/fmed.2025.1618550},
  pmid    = {40612575},
  url     = {https://doi.org/10.3389/fmed.2025.1618550}
}

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