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A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system.

Overview

Authors: Md Taimur Ahad1,2, Bo Song3, Yan Li1
ORCID iDs: Md Taimur Ahad
  1. School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
  2. Department of Management, North South University, Dhaka, Bangladesh
  3. School of Engineering, University of Southern Queensland, Toowoomba, Australia
Institutions: North South University (Bangladesh); University of Southern Queensland (Australia)
Journal: Scientific reports, volume 16, issue 1, article 21718
Dates: received 28 September 2025; accepted 27 April 2026; published online 12 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-51236-0 · PMID 42120538 · PMCID PMC13358162 · OpenAlex W7160909898
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: structural MRI / diffusion (modality), human (organism), other condition (population)
Methods: Graphs, Machine learning
Keywords: Brain tumor detection, Convolutional neural network, CNN, Lightweight CNN, SHAP, LIME, GRAD-CAM, Cancer, Computational biology and bioinformatics, Engineering, Health care, Mathematics and computing
MeSH: Brain Neoplasms*, Internet of Things*, Neural Networks, Computer*, Convolutional Neural Networks, Humans, Magnetic Resonance Imaging (* major topic)
Topic: Brain Tumor Detection and Classification (Neurology, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 101 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Data

Datasets cited

Data availability statement

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Read it in the paper: doi.org/10.1038/s41598-026-51236-0.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 12 keywords, 6 MeSH terms, 34 references.

Cite

This paper

Ahad, M. T., Song, B., & Li, Y. (2026). A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system. Scientific reports, 16(1), 21718. https://doi.org/10.1038/s41598-026-51236-0

BibTeX

@article{ahad2026minimal,
author = {Ahad, Md Taimur and Song, Bo and Li, Yan},
title = {{A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {21718},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-51236-0},
url = {https://doi.org/10.1038/s41598-026-51236-0},
pmid = {42120538},
pmcid = {PMC13358162}
}

RIS

TY - JOUR
AU - Ahad, Md Taimur
AU - Song, Bo
AU - Li, Yan
TI - A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/12
VL - 16
IS - 1
SP - 21718
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-51236-0
UR - https://doi.org/10.1038/s41598-026-51236-0
LA - en
ER -

CSL-JSON

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