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A Trustworthy Clinical Decision Support Framework for Brain Tumor MRI Classification: Integrating Attention-Guided Learning, Explainable AI, and Out-of-Distribution… — arXiv:2611.00034

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00034
License (as archived): CC0-1.0
Archived: 2026-11-03T11:04:29.542Z
SHA-256 of the archived text: 1251706bd7d63a06b5bfbd93c5dab3f85a833c48ae4f0eb09dd70168eaf121d8
Stable URL of this page: https://metraipolis.com/documento/dc_a6650d0dfd66

How to cite

A Trustworthy Clinical Decision Support Framework for Brain Tumor MRI Classification: Integrating Attention-Guided Learning, Explainable AI, and Out-of-Distribution… — arXiv:2611.00034 — arXiv (en), https://arxiv.org/abs/2611.00034 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a6650d0dfd66 (Metraipolis archive dc_a6650d0dfd66, sha256 1251706bd7d63a06b5bfbd93c5dab3f85a833c48ae4f0eb09dd70168eaf121d8)

BibTeX

@misc{dca6650d0dfd66,
  title = {A Trustworthy Clinical Decision Support Framework for Brain Tumor MRI Classification: Integrating Attention-Guided Learning, Explainable AI, and Out-of-Distribution… — arXiv:2611.00034},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00034},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a6650d0dfd66; sha256 1251706bd7d63a06b5bfbd93c5dab3f85a833c48ae4f0eb09dd70168eaf121d8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00034. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Deep learning models for brain tumor diagnosis need to do more than achieve high classification accuracy. For practical use, they should also provide understandable predictions, recognize unsuitable inputs, and work within a usable clinical system. This paper presents a framework for classifying brain MRI into four categories: glioma, meningioma, pituitary tumor, and no tumor. The model uses InceptionResNetV2 with a Convolutional Block Attention Module (CBAM), while Grad-CAM provides visual explanations of its predictions. A two-stage out-of-distribution (OOD) mechanism screens unsuitable inputs using an initial grayscale check followed by Mahalanobis distance, softmax confidence, and predictive entropy. These components are brought together in NeuroScan AI, a real-time web application. Evaluated on a Mendeley brain tumor MRI dataset of 12,064 images, the framework achieved 98.70% validation accuracy, 96.73% test accuracy, and a macro-average AUC of 0.9965. The OOD pipeline achieved 99% detection accuracy on a preliminary 100-image benchmark. Grad-CAM visualizations showed qualitative correspondence with clinically established tumor locations across all four classes. Overall, the work brings classification, explainability, input validation, and deployment into one framework, while recognizing that broader clinical and OOD validation is still required.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.00034 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:59:45.904Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).