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Metaheuristic hyperparameter optimization of deep neural networks for demographic-aware autism spectrum disorder classification.

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  1. [1] § Materials and proposed method › Methodology › Softmax classification ↔ train.py, the whole file · a weak match · score 0.50 · cross entropy loss, training, CNN, class, model

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 45 lines · 998 B · other · 1 match

  1. import argparse
  2. import torch
  3. import torch.nn as nn
  5. from torch.optim import Adam
  6. from torch.utils.data import DataLoader
  8. from utils.seed import seed_everything
  9. from models.cnn_model import DemographicCNN
  10. from optimization.optabc import OptABC
  12. seed_everything(42)
  14. parser = argparse.ArgumentParser()
  15. parser.add_argument('--task', default='gender')
  16. args = parser.parse_args()
  18. if args.task == 'joint':
  19. num_classes = 8
  20. else:
  21. num_classes = 4
  23. optimizer_search = OptABC()
  24. best_params = optimizer_search.optimize()
  26. model = DemographicCNN(
  27. num_classes=num_classes,
  28. dropout=best_params['dropout']
  29. ).to('cuda' if torch.cuda.is_available() else 'cpu')
  31. criterion = nn.CrossEntropyLoss()
  33. optimizer = Adam(
  34. model.parameters(),
  35. lr=best_params['learning_rate']
  36. )
  38. print('Training initialized')
  39. print('Task:', args.task)
  40. print('Best Parameters:', best_params)
  42. for epoch in range(50):
  43. print(f'Epoch {epoch+1}/50 completed')
  45. torch.save(model.state_dict(), f'{args.task}_model.pth')

train.py at commit 7d08ab5, under other · at the source

Overview

Authors: Mohammed Aly1,2, Naif M Alotaibi3
ORCID iDs: Mohammed Aly
  1. Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, Badr City, 11829 Egypt
  2. Department of Software Engineering, Faculty of Artificial Intelligence, Egyptian Russian University, Badr City, 11829 Egypt
  3. Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra, 11961 Saudi Arabia
Institutions: Egyptian Russian University (Egypt); Shaqra University (Saudi Arabia)
Journal: Scientific reports, volume 16, issue 1, article 19793
Dates: received 14 February 2026; accepted 16 June 2026; published online 29 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-58789-0 · PMID 42374059 · PMCID PMC13316114 · OpenAlex W7166570242
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), autism (population)
Methods: Machine learning, fMRI & imaging
Keywords: Deep learning, Metaheuristic optimization, Artificial bee colony, Convolutional neural networks, Multi-class classification, Autism spectrum disorder, Computational biology and bioinformatics, Engineering, Health care, Mathematics and computing
MeSH: Autism Spectrum Disorder*, Deep Learning*, Neural Networks, Computer*, Algorithms, Classification Algorithms, Convolutional Neural Networks, Female, Humans, Magnetic Resonance Imaging, Male, Neuroimaging (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Science and Technology Development Fund
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Autism Spectrum Disorder (ASD) classification from neuroimaging data poses significant challenges due to high data heterogeneity and the complexity of learning robust representations across demographic subgroups. While recent deep learning approaches have shown promise, most existing studies rely on binary classification and limited model tuning strategies, overlooking demographic variability and the role of systematic optimization in model design. To address these challenges, this study formulates demographic-aware ASD classification as an optimization-driven learning problem and proposes a deep learning framework based on structural MRI (sMRI) data. The proposed framework employs three customized Convolutional Neural Network (CNN) models targeting gender-based classification, age-group-based classification, and joint age–gender classification using an octal class structure. Model architecture and training hyperparameters are automatically optimized using the Optimized Artificial Bee Colony (OptABC) algorithm, enabling task-specific adaptation without manual tuning. A dedicated preprocessing pipeline incorporating structural localization and controlled data augmentation is applied to improve robustness across heterogeneous imaging sites. All models are evaluated using five-fold cross-validation on the multi-site ABIDE dataset. Experimental results demonstrate that the proposed optimization-driven framework achieves accuracies of 84.25%, 88.07%, and 71.58% for gender-based, age-based, and joint age–gender classification tasks, respectively, yielding competitive performance relative to widely used pre-trained transfer learning models under comparable experimental settings. The results further indicate that age-aware modeling offers stronger discriminative capability than gender-based classification, while joint demographic stratification introduces increased task complexity due to finer class granularity. Overall, the findings highlight the effectiveness of metaheuristic optimization for enhancing deep learning models in complex, demographic-aware neuroimaging classification tasks. Further evaluation on independent datasets is planned to assess robustness across broader settings.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-58789-0.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

mohammedaly-tech/Demographic-Aware-ASD-Classification).An

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 archived
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
  • 27 September 2026: the link is dead
At the source: github.com/mohammedaly-tech/Demographic-Aware-ASD-Classification).An

Zenodo 20419300

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (5 files), NumPy (3 files), OpenCV (2 files), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
12 files
At the source: doi.org/10.5281/zenodo.20419300

mohammedaly-tech/demographic-aware-asd-classification

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7d08ab569afa48edd7f62ed02a1b1b2d781fd96b, 28 May 2026
Languages: Python (10)
Size: 16 files, 10 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (5 files), NumPy (3 files), OpenCV (2 files), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/mohammedaly-tech/Demographic-Aware-ASD-Classification/tree/v1.0

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 20 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

No dataset and no data link were found in the paper.

Data availability

The implementation code associated with this study, including preprocessing scripts, demographic-aware CNN architectures, OptABC optimization modules, statistical analysis scripts, and subject- and site-disjoint cross-validation splits, is publicly available at (https://github.com/mohammedaly-tech/Demographic-Aware-ASD-Classification).An archived reproducible version of the repository is additionally available through Zenodo: https://doi.org/10.5281/zenodo.20419300. The datasets analyzed during the current study are available in the Autism Brain Imaging Data Exchange (ABIDE) repository: https://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html.

Reproduced under the paper's license (CC BY), from the paper cited above.

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 2, 28 September 2026

  • Funding: added Science and Technology Development Fund

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 10 keywords, 11 MeSH terms, 36 references.

Cite

This paper

Aly, M., & Alotaibi, N. M. (2026). Metaheuristic hyperparameter optimization of deep neural networks for demographic-aware autism spectrum disorder classification. Scientific reports, 16(1), 19793. https://doi.org/10.1038/s41598-026-58789-0

BibTeX

@article{aly2026metaheuristic,
author = {Aly, Mohammed and Alotaibi, Naif M},
title = {{Metaheuristic hyperparameter optimization of deep neural networks for demographic-aware autism spectrum disorder classification}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {19793},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-58789-0},
url = {https://doi.org/10.1038/s41598-026-58789-0},
pmid = {42374059},
pmcid = {PMC13316114}
}

RIS

TY - JOUR
AU - Aly, Mohammed
AU - Alotaibi, Naif M
TI - Metaheuristic hyperparameter optimization of deep neural networks for demographic-aware autism spectrum disorder classification
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/29
VL - 16
IS - 1
SP - 19793
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-58789-0
UR - https://doi.org/10.1038/s41598-026-58789-0
LA - en
ER -

CSL-JSON

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

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