OSCR

Depression detection from multimodalities based on LeNet with hunter-geese optimization.

Overview

Authors: Pradeep G1, Oswalt Manoj S2, S. Karthikeyini3
  1. Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Anna University,Kuniyamuthur, Coimbatore, 641008 India
  2. Associate Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology,Coimbatore, 641008 India
  3. Assistant Professor, Department of CSE, Sri Ramakrishna Institute of Technology,Coimbatore, 641010 India
Institutions: Anna University, Chennai (India)
Journal: Scientific reports, volume 16, issue 1, article 26047
Dates: received 12 February 2026; accepted 11 May 2026; published online 14 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-53279-9 · PMID 42289455 · PMCID PMC13490529 · OpenAlex W7164739070
Open access: gold, a free copy (OpenAlex)
Status: dead link
Categories: structural MRI / diffusion (modality), human (organism), depression (population), cognitive (subfield)
Methods: Statistics, Machine learning, Spectral & time-frequency, fMRI & imaging, Preprocessing
Keywords: Multimodality, Magnetic resonance imaging, Speech signal, Depression detection, Deep learning, Computational biology and bioinformatics, Diseases, Engineering, Health care, Mathematics and computing
MeSH: Depression*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Algorithms, Brain, Humans, Speech (* major topic)
Topic: Emotion and Mood Recognition (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Citations: not cited yet (Europe PMC); 58 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

No file of the authors' code could be read here: it is described below, and read at its source.

kaggle.com/code/rizkyyk

License: none: the authors keep all their rights
State: the link is dead, verified on 27 September 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link is dead (HTTP 404)
  • 27 September 2026: the link is dead (HTTP 404)
At the source: kaggle.com/code/rizkyyk/speech-depression-detection/input

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Code and data availability statement

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

Read it in the paper: doi.org/10.1038/s41598-026-53279-9.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 10 keywords, 7 MeSH terms, 27 references.

Cite

This paper

G, P., S, O. M., & Karthikeyini, S. (2026). Depression detection from multimodalities based on LeNet with hunter-geese optimization. Scientific reports, 16(1), 26047. https://doi.org/10.1038/s41598-026-53279-9

BibTeX

@article{g2026depression,
author = {G, Pradeep and S, Oswalt Manoj and Karthikeyini, S.},
title = {{Depression detection from multimodalities based on LeNet with hunter-geese optimization}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {26047},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-53279-9},
url = {https://doi.org/10.1038/s41598-026-53279-9},
pmid = {42289455},
pmcid = {PMC13490529}
}

RIS

TY - JOUR
AU - G, Pradeep
AU - S, Oswalt Manoj
AU - Karthikeyini, S.
TI - Depression detection from multimodalities based on LeNet with hunter-geese optimization
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/14
VL - 16
IS - 1
SP - 26047
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-53279-9
UR - https://doi.org/10.1038/s41598-026-53279-9
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-53279-9",
"type": "article-journal",
"title": "Depression detection from multimodalities based on LeNet with hunter-geese optimization",
"container-title": "Scientific reports",
"author": [
{
"family": "G",
"given": "Pradeep"
},
{
"family": "S",
"given": "Oswalt Manoj"
},
{
"family": "Karthikeyini",
"given": "S."
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "26047",
"DOI": "10.1038/s41598-026-53279-9",
"PMID": "42289455",
"PMCID": "PMC13490529",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-53279-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
14
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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:10.1038/s41598-026-58491-1 [code]
Cross-attention guided explainable deep transformer model for multi-level classification of rare neurological disorders using MRI images.
Journal: Scientific reports
In common: structural MRI / diffusion, 1 reference
[2] doi:10.1038/s41598-026-51542-7
Brain tumor classification using fractional Laplacian image enhancement and vision transformer.
Journal: Scientific reports
In common: structural MRI / diffusion, 1 reference
[3] doi:10.1002/acm2.70560 [code]
Hybrid CNN-GCN framework for brain tumor MRI classification: A graph-based approach to smart healthcare diagnostics.
Journal: Journal of applied clinical medical physics
In common: structural MRI / diffusion, 1 reference
[4] doi:10.1038/s41598-026-43329-7
Hybrid Aquila optimizer-Harris Hawks optimization for CNN hyperparameter tuning in brain tumor classification.
Journal: Scientific reports
In common: structural MRI / diffusion, 1 reference
[5] 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: 1 reference
[6] doi:10.1038/s41598-026-62977-3
Hyperparameter-optimized deep learning for next-day pH forecasting in distributed water quality monitoring systems.
Journal: Scientific reports
In common: 1 reference
[7] doi:10.1162/imag.a.1363 [code]
Social approach-avoidance conflict: Behavioural, emotional, and neural correlates along a depression-social anxiety spectrum.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: depression, cognitive
[8] doi:10.1001/jamanetworkopen.2026.33507 [code]
Illness Severity and Longitudinal Gray Matter Volumes Among People With Major Depressive Disorder.
Journal: JAMA network open
In common: depression, structural MRI / diffusion
[9] doi:10.1016/j.nicl.2026.104053 [code]
Childhood white matter organization predicts adolescent internalizing problems among youth with and without ADHD.
Journal: NeuroImage. Clinical
In common: depression, structural MRI / diffusion
[10] doi:10.1093/aje/kwag135 [code]
Depressive symptoms and neuroimaging markers of brain aging in an ethno-racially diverse sample: a Bayesian analysis.
Journal: American journal of epidemiology
In common: depression, structural MRI / diffusion

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.