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Hybrid Vi+ECNN framework for advanced ADHD diagnostic accuracy in medical imaging.

Code ↔ Paper

10 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 10 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Proposed model › Hybrid ViT+ECNN model ↔ Combined.ipynb, lines 119–157 · score 0.71 · linearly projected, classification layer, CLS, Patch16, pretrained, token
  2. [2] § Experimental results › Hyperparameter settings ↔ Comments response .ipynb, lines 227–299 · score 0.65 · AdamW, cross validation, weight decay, stratified, memory, metrics
  3. [3] § Experimental results › Hyperparameter settings ↔ Comments__response_ v2.ipynb, lines 227–299 · score 0.65 · AdamW, cross validation, weight decay, stratified, memory, metrics
  4. [4] § Proposed model › Data preprocessing ↔ Combined.ipynb, lines 24–30 · score 0.62 · Quantile Histogram Equalization, median filtering, QHED
  5. [5] § Proposed model › Data preprocessing ↔ ECNN.ipynb, lines 23–29 · score 0.62 · Quantile Histogram Equalization, median filtering, QHED
  6. [6] § Proposed model › Hybrid ViT+ECNN model ↔ Combined.ipynb, lines 119–157 · score 0.61 · fully connected layer, ReLU, concatenation, module, ViT, ECNN
  7. [7] § Proposed model › Data augmentation ↔ ViT.ipynb, the whole file · a weak match · score 0.56 · cosine annealing learning, scheduler, cross, loss, splits, validation
  8. [8] § Proposed model › Data augmentation ↔ Combined.ipynb, lines 185–188 · score 0.52 · cosine annealing learning, scheduler, cross, loss, Model
  9. [9] § Experimental results › Structural ablation study for module necessity validation ↔ Comments__response_ v2.ipynb, lines 663–782 · score 0.51 · structural ablation, necessity, score, recall, configuration, precision
  10. [10] § Experimental results › Structural ablation study for module necessity validation ↔ Comments__response_ v2.ipynb, lines 663–782 · score 0.51 · structural ablation, QHED preprocessing, necessity, fusion, configuration, modules

Paper

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

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The authors' code

Jupyter notebook · 260 lines · 12 KB · no license · 4 matches

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It can be read at the source: Combined.ipynb.

Overview

Authors: Shaymaa E Sorour1, Lamia Hassan1, Osman Elwasila1, Tsunenori Mine2, Mohamed Ali Nagy Elmaadaway3
  1. Department of Management Information Systems, School of Business, King Faisal University, 31982 Al-Ahsa, Saudi Arabia
  2. Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, 819-0395 Japan
  3. Research Center of Excellence in Science and Mathematics Education Development, DSR, King Saud University, 2458, 11451 Riyadh, Saudi Arabia
Institutions: King Faisal University (Saudi Arabia); Kyushu University (Japan); King Saud University (Saudi Arabia)
Journal: Scientific reports, volume 16, issue 1, article 19017
Dates: received 12 July 2025; accepted 23 April 2026; published online 2 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-50791-w · PMID 42069836 · PMCID PMC13280160 · OpenAlex W7159982485
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), ADHD (population), clinical / translational (subfield)
Methods: Machine learning, Statistics, Connectivity, Preprocessing
Keywords: Attention-deficit/hyperactivity disorder (ADHD), Deep learning, Vision transformer (ViT), Enhanced convolutional neural network (ECNN), Medical image classification, Pediatric MRI, Hybrid architectures, Computational biology and bioinformatics, Diseases, Engineering, Health care, Mathematics and computing, Medical research, Neuroscience
MeSH: Attention Deficit Disorder with Hyperactivity*, Brain*, Deep Learning*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Child, Convolutional Neural Networks, Humans (* major topic)
Topic: Attention Deficit Hyperactivity Disorder (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Deanship of Scientific Research, King Faisal University (KFU254121)
Citations: not cited yet (Europe PMC); 66 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

shaymaasorour/Hybrid-ViT-ECNN-Framework-for-Advanced-ADHD-Diagnostic-Accuracy-in-Medical-Imaging

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3a399cf849f18af7132ca1882adc4933baa6b856, 22 April 2026
Languages: Jupyter (5)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 5 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (5 files), NumPy (4 files), Pillow (4 files), Matplotlib (2 files), OpenCV (2 files), pandas (2 files), scikit-learn (2 files), seaborn (2 files), Hugging Face Transformers (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
6 files, not copied: shown from their source

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At the source: github.com/shaymaasorour/Hybrid-ViT-ECNN-Framework-for-Advanced-ADHD-Diagnostic-Accuracy-in-Medical-Imaging

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;
  • 5 scripts, each with its path and the digest of its content;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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/s41598-026-50791-w.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 14 keywords, 8 MeSH terms, 1 funder, 61 references.

Cite

This paper

Sorour, S. E., Hassan, L., Elwasila, O., Mine, T., & Elmaadaway, M. A. N. (2026). Hybrid Vi+ECNN framework for advanced ADHD diagnostic accuracy in medical imaging. Scientific reports, 16(1), 19017. https://doi.org/10.1038/s41598-026-50791-w

BibTeX

@article{sorour2026hybrid,
author = {Sorour, Shaymaa E and Hassan, Lamia and Elwasila, Osman and Mine, Tsunenori and Elmaadaway, Mohamed Ali Nagy},
title = {{Hybrid Vi+ECNN framework for advanced ADHD diagnostic accuracy in medical imaging}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {19017},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-50791-w},
url = {https://doi.org/10.1038/s41598-026-50791-w},
pmid = {42069836},
pmcid = {PMC13280160}
}

RIS

TY - JOUR
AU - Sorour, Shaymaa E
AU - Hassan, Lamia
AU - Elwasila, Osman
AU - Mine, Tsunenori
AU - Elmaadaway, Mohamed Ali Nagy
TI - Hybrid Vi+ECNN framework for advanced ADHD diagnostic accuracy in medical imaging
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/02
VL - 16
IS - 1
SP - 19017
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-50791-w
UR - https://doi.org/10.1038/s41598-026-50791-w
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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