OSCR

Quantum SVM-driven framework for accurate brain stroke classification.

Overview

Authors: S. Baghavathi Priya1, M. Rajamanogaran2, Krithikha Sanju Saravanan3, S. Priyanga1
  1. Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham,Chennai, Tamilnadu India
  2. Department of Computer Science, Periyar Government Arts College, Cuddalore, Tamilnadu India
  3. Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering,Chennai, India
Institutions: Amrita Vishwa Vidyapeetham (India); Sri Sivasubramaniya Nadar College of Engineering (India)
Journal: Scientific reports, volume 16, issue 1, article 21512
Dates: received 24 November 2025; accepted 30 April 2026; published online 11 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-51942-9 · PMID 42115706 · PMCID PMC13350720 · OpenAlex W7160860873
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: structural MRI / diffusion (modality), human (organism), stroke (population), methods / tools (subfield)
Methods: Statistics, Machine learning, Spectral & time-frequency, Connectivity, fMRI & imaging, Physiology & signal measures
Keywords: Brain stroke classification, Quantum support vector machine (QSVM), Ischemic stroke, Hemorrhagic stroke, Quantum computing, Medical image classification, Computational biology and bioinformatics, Engineering, Mathematics and computing, Neurology, Neuroscience
MeSH: Stroke*, Support Vector Machine*, Algorithms, Brain, Classification Algorithms, Humans, Magnetic Resonance Imaging, Quantum Theory (* major topic)
Topic: Quantum Computing Algorithms and Architecture (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: Amrita Vishwa Vidyapeetham, Chennai
Citations: not cited yet (Europe PMC); 48 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

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41598-026-51942-9.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 11 keywords, 8 MeSH terms, 1 funder, 47 references.

Cite

This paper

Priya, S. B., Rajamanogaran, M., Saravanan, K. S., & Priyanga, S. (2026). Quantum SVM-driven framework for accurate brain stroke classification. Scientific reports, 16(1), 21512. https://doi.org/10.1038/s41598-026-51942-9

BibTeX

@article{priya2026quantum,
author = {Priya, S. Baghavathi and Rajamanogaran, M. and Saravanan, Krithikha Sanju and Priyanga, S.},
title = {{Quantum SVM-driven framework for accurate brain stroke classification}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {21512},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-51942-9},
url = {https://doi.org/10.1038/s41598-026-51942-9},
pmid = {42115706},
pmcid = {PMC13350720}
}

RIS

TY - JOUR
AU - Priya, S. Baghavathi
AU - Rajamanogaran, M.
AU - Saravanan, Krithikha Sanju
AU - Priyanga, S.
TI - Quantum SVM-driven framework for accurate brain stroke classification
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/11
VL - 16
IS - 1
SP - 21512
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-51942-9
UR - https://doi.org/10.1038/s41598-026-51942-9
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-51942-9",
"type": "article-journal",
"title": "Quantum SVM-driven framework for accurate brain stroke classification",
"container-title": "Scientific reports",
"author": [
{
"family": "Priya",
"given": "S. Baghavathi"
},
{
"family": "Rajamanogaran",
"given": "M."
},
{
"family": "Saravanan",
"given": "Krithikha Sanju"
},
{
"family": "Priyanga",
"given": "S."
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "21512",
"DOI": "10.1038/s41598-026-51942-9",
"PMID": "42115706",
"PMCID": "PMC13350720",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-51942-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
11
]
]
}
}

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3390/s26165021
Automatic Segmentation of Ischaemic Stroke Lesions Using Transformers and Convolutional Neural Networks Applied to Multimodal Neuroimaging.
Journal: Sensors (Basel, Switzerland)
In common: stroke, methods / tools, 1 reference
[2] doi:10.3390/diagnostics16152407
BG-YOLO11s: Boundary-Guided YOLO11 with Bézier Contour Augmentation for Brain Tumour Segmentation in T1-CE MRI.
Journal: Diagnostics (Basel, Switzerland)
In common: methods / tools, structural MRI / diffusion, 1 reference
[3] doi:10.1038/s41598-026-48825-4
YOLO-LS: a novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging.
Journal: Scientific reports
In common: methods / tools, structural MRI / diffusion, 1 reference
[4] doi:10.1038/s41598-026-55903-0 [code]
Towards trustworthy brain stroke diagnosis using a lightweight explainable deep learning framework for CT imaging.
Journal: Scientific reports
In common: stroke, 1 reference
[5] doi:10.1038/s41598-026-60334-y
Enhancing brain tumor detection through deep learning and explainable AI techniques.
Journal: Scientific reports
In common: structural MRI / diffusion, 1 reference
[6] doi:10.1016/j.nicl.2026.104001 [code]
Effect of vascular lesion preprocessing on Brain Intensity AbNormality Classification Algorithm (BIANCA) white matter hyperintensity segmentation.
Journal: NeuroImage. Clinical
In common: stroke, methods / tools, structural MRI / diffusion
[7] doi:10.1186/s12938-026-01555-0 [code]
Incorporating normal periventricular changes for enhanced pathological white matter hyperintensity segmentation: on multiclass deep learning approaches.
Journal: Biomedical engineering online
In common: stroke, methods / tools, structural MRI / diffusion
[8] doi:10.3390/diagnostics16172776
A Resource-Efficient Hybrid Deep Learning Framework with External Validation for Intracranial Hemorrhage Detection in CT Scans.
Journal: Diagnostics (Basel, Switzerland)
In common: 1 reference
[9] doi:10.1038/s41597-026-07787-y
A multi-paradigm and longitudinal EEG dataset including the "sixth-finger" and "affected-hand" motor imagery of stroke patients.
Journal: Scientific data
In common: stroke, methods / tools
[10] doi:10.1038/s41598-026-53829-1 [code]
Attention U-Net with differential privacy in federated learning framework for brain stroke lesion segmentation.
Journal: Scientific reports
In common: stroke, methods / tools

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.