{
  "abstract": "Deep learning (DL) models for brain tumour segmentation (BTS) typically produce a fixed decision boundary and do not allow the uncertainty they estimate to impact the segmentation decision (SD). This work presents a FUZIONet-Med: A Fuzzy Uncertainty-Adaptive IoT Edge Intelligence Model for Brain Tumor Segmentation in which decomposed voxel-level uncertainty governs the SD within a single network. Monte Carlo dropout (MCD) generates epistemic and aleatoric uncertainty maps, which drive a Mamdani fuzzy inference system through an asymmetric nine-rule base to produce a voxel-level adaptive threshold field. This field modulates the encoder skip connections through a fuzzy-guided attention mechanism (FGAM) and, at inference, replaces the conventional fixed threshold as the voxel-level decision boundary, so that the segmentation boundary varies with local uncertainty. The model is deployed within a three-tier IoT architecture in which edge nodes (EN) perform acquisition, pre-processing, and inference, and a practical Byzantine fault-tolerant (PBFT)-secured protocol governs federated aggregation. On the BraTS 2013 benchmark under repeated stratified cross-validation, the model attained Dice scores of 0.908, 0.851, and 0.769 for the whole tumour (WT), tumour core (TC), and active tumour (AT) regions, with a 95th-percentile Hausdorff distance of 10.14 mm and an uncertainty calibration error (UCE) of 0.121, exceeding 6 baselines on every metric with Holm-adjusted statistical significance. In this study, we performed an external evaluation on 1,221 independent BraTS 2021 subjects using a model configuration fixed exclusively to the BraTS 2013 development protocol, without retraining, fine-tuning, or external-cohort-driven parameter optimization. Full-resolution inference was completed in 2930 ms at 28.9 W on an NVIDIA Jetson AGX Xavier EN, within a 30 W envelope. The results indicate that coupling decomposed uncertainty to the inference-time decision boundary through in-network fuzzy control improves segmentation accuracy and calibration while remaining deployable on clinical edge hardware.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Vijaya Bhaskar Sadu"
    },
    {
      "name": "Rukmani Devi Sethuraman"
    },
    {
      "name": "Kamalakar Ramineni"
    },
    {
      "name": "Nateshan Venkatraman Sreeram Natteshan"
    },
    {
      "name": "Vinoth Raja"
    },
    {
      "name": "Meshal Ghalib Alharbi"
    },
    {
      "name": "Rajamurugan Anbuchelvan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4m2ewcmchdn5t.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-74130-1",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-74130-1",
  "id": "4m2ewcmchdn5t",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-74130-1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
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      "lcc",
      "R"
    ],
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      "openalex",
      "2808|Brain Tumor Detection and Classification"
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  "pdf_url": "https://www.nature.com/articles/s41598-026-74130-1.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/4m2ewcmchdn5t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.1038/s41598-026-74130-1",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-74130-1"
    },
    {
      "first_seen": "2026-10-30T13:45:59Z",
      "native_id": "da0ce371faf1481c916c94fa8ba86066",
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      "url": "https://doi.org/10.1038/s41598-026-74130-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2808|Brain Tumor Detection and Classification",
      "subfield": "neurology"
    }
  ],
  "title": "Fuzzy-logic enhanced internet of things model for uncertainty-adaptive medical image segmentation",
  "title_text": "Fuzzy-logic enhanced internet of things model for uncertainty-adaptive medical image segmentation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:59Z",
  "url": "https://research.draconitic.com/paper/4m2ewcmchdn5t",
  "venue": "Scientific Reports",
  "versions": []
}