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FMCL: a transformer-based feature-map classifier learning approach for enhanced brain tumor detection in MRI.

Code ↔ Paper

1 match 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 1 match
  1. [1] § Proposed feature-map feature map-based learning model method › Transformer learning ↔ train.py, lines 9–49 · score 0.56 · cross entropy loss, zero, training, feature map, transformer, classification

Paper

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The authors' code

Python · 49 lines · 1.5 KB · no license · 1 match

  1. import torch
  2. import torch.nn as nn
  3. import torch.optim as optim
  4. from torch.utils.data import DataLoader
  5. from torchvision import datasets, transforms
  6. from model import FeatureMapContrast, TransformerClassifier
  7. import yaml
  9. # Load config
  10. with open("configs/config.yaml") as f:
  11. config = yaml.safe_load(f)
  13. device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  15. transform = transforms.Compose([
  16. transforms.Resize((224, 224)),
  17. transforms.ToTensor(),
  18. ])
  20. train_dataset = datasets.ImageFolder(config["train_dir"], transform=transform)
  21. train_loader = DataLoader(train_dataset, batch_size=config["batch_size"], shuffle=True)
  23. feature_model = FeatureMapContrast().to(device)
  24. classifier = TransformerClassifier().to(device)
  26. criterion = nn.CrossEntropyLoss()
  27. optimizer = optim.Adam(list(feature_model.parameters()) +
  28. list(classifier.parameters()),
  29. lr=config["learning_rate"])
  31. for epoch in range(config["epochs"]):
  32. feature_model.train()
  33. classifier.train()
  34. total_loss = 0
  36. for images, labels in train_loader:
  37. images, labels = images.to(device), labels.to(device)
  39. features = feature_model(images)
  40. outputs = classifier(features)
  42. loss = criterion(outputs, labels)
  43. optimizer.zero_grad()
  44. loss.backward()
  45. optimizer.step()
  47. total_loss += loss.item()
  49. print(f"Epoch [{epoch+1}/{config['epochs']}], Loss: {total_loss:.4f}")

train.py at commit b2b9678, no license · at the source

Overview

Authors: Turki M Alanazi1
ORCID iDs: Turki M Alanazi
  1. Department of Electrical Engineering, College of Engineering, University of Hafr Al Batin, 39524 Hafr Al Batin, Saudi Arabia
Institutions: University of Hafr Al-Batin (Saudi Arabia)
Journal: Scientific reports, volume 16, issue 1, article 12571
Dates: received 8 November 2025; accepted 25 February 2026; published online 8 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-42450-x · PMID 41796189 · PMCID PMC13086971 · OpenAlex W7134187031
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging
Keywords: Brain tumor, Feature map, Magnetic resonance imaging, SoftMax function, Transformer learning, Cancer, Computational biology and bioinformatics, Engineering, Mathematics and computing, Medical research
MeSH: Brain Neoplasms*, Image Interpretation, Computer-Assisted*, Image Processing, Computer-Assisted*, Machine Learning*, Magnetic Resonance Imaging*, Classification Algorithms, Convolutional Neural Networks, Humans (* major topic)
Topic: Brain Tumor Detection and Classification (Neurology, Neuroscience), according to OpenAlex
Funding: Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia (0018-1446-S); Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia (0018-1446-S)
Citations: not cited yet (Europe PMC); 51 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.

Repositories

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

Zenodo 18740802

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (4 files), scikit-learn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
5 files
At the source:

pavimounisri-byte/FMCL-Brain-Tumor-MRI

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: b2b967857a30514dcd8d81b40f3ce1220d99ea4d, 18 February 2026
Languages: Python (5)
Size: 9 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: environment (requirements.txt)
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (4 files), scikit-learn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
5 files
At the source: github.com/pavimounisri-byte/FMCL-Brain-Tumor-MRI

Code availability statement

The paper has a code 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/s41598-026-42450-x.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 10 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.

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Data

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Data availability statement

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

Read it in the paper: doi.org/10.1038/s41598-026-42450-x.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 10 keywords, 8 MeSH terms, 2 funders, 27 references.

Cite

This paper

Alanazi, T. M. (2026). FMCL: a transformer-based feature-map classifier learning approach for enhanced brain tumor detection in MRI. Scientific reports, 16(1), 12571. https://doi.org/10.1038/s41598-026-42450-x

BibTeX

@article{alanazi2026fmcl,
author = {Alanazi, Turki M},
title = {{FMCL: a transformer-based feature-map classifier learning approach for enhanced brain tumor detection in MRI}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {12571},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-42450-x},
url = {https://doi.org/10.1038/s41598-026-42450-x},
pmid = {41796189},
pmcid = {PMC13086971}
}

RIS

TY - JOUR
AU - Alanazi, Turki M
TI - FMCL: a transformer-based feature-map classifier learning approach for enhanced brain tumor detection in MRI
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/08
VL - 16
IS - 1
SP - 12571
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-42450-x
UR - https://doi.org/10.1038/s41598-026-42450-x
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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