---
title: "ashkhagan/figshare-brain-tumor-dataset: papers citing it"
description: "3 papers in the tracked journals link to the Kaggle dataset ashkhagan/figshare-brain-tumor-dataset in their Crossref records, the first in 2024; most of them (2) in 2024."
url: https://scholar4ai.com/code/kaggle-ashkhagan-figshare-brain-tumor-dataset
markdown_url: https://scholar4ai.com/code/kaggle-ashkhagan-figshare-brain-tumor-dataset.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "3 papers in the tracked journals link to the Kaggle dataset ashkhagan/figshare-brain-tumor-dataset in their Crossref records, the first in 2024; most of them (2) in 2024. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# ashkhagan/figshare-brain-tumor-dataset: papers linking to it

Research papers in the 480 tracked journals that link to ashkhagan/figshare-brain-tumor-dataset (https://www.kaggle.com/datasets/ashkhagan/figshare-brain-tumor-dataset), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Kaggle
- https://www.kaggle.com/datasets/ashkhagan/figshare-brain-tumor-dataset

All repositories: https://scholar4ai.com/code.md

## Key findings

- 3 papers in the tracked journals link to the Kaggle dataset ashkhagan/figshare-brain-tumor-dataset in their Crossref records, the first in 2024; most of them (2) in 2024.
- By category: AI (3); the journals with the most are Neural Computing and Applications (2) and Knowledge-Based Systems (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links ashkhagan/figshare-brain-tumor-dataset.

## By year

*Papers linking to ashkhagan/figshare-brain-tumor-dataset by publication year*

| Year | Papers |
|---|---:|
| 2024 | 2 |
| 2025 | 1 |

## Journals

*Journals whose papers link to ashkhagan/figshare-brain-tumor-dataset most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm](https://doi.org/10.1016/j.knosys.2024.112362) (Knowledge-Based Systems, 2024)
  - [Enhanced brain tumor detection and classification using a deep image recognition generative adversarial network (DIR-GAN): a comparative study on MRI, X-ray, and FigShare datasets](https://doi.org/10.1007/s00521-025-11031-w) (Neural Computing and Applications, 2025) — cites the paper above

## Papers

*Papers linking to ashkhagan/figshare-brain-tumor-dataset, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-08-08 | [Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm](https://doi.org/10.1016/j.knosys.2024.112362) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | C. Moorthy, Jampani Chandra Sekhar, Sibghatullah Inayatullah Khan et al. | 18 | cites it |
| 2025-02-28 | [Enhanced brain tumor detection and classification using a deep image recognition generative adversarial network (DIR-GAN): a comparative study on MRI, X-ray, and FigShare datasets](https://doi.org/10.1007/s00521-025-11031-w) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | S. Karpakam, N. Kumareshan | 12 | cites it |
| 2024-05-23 | [ABES: attention bi-directional ensemble SVM for early detection of brain tumors](https://doi.org/10.1007/s00521-024-09688-w) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Erana Veerappa Dinesh Subramaniam, K. Valarmathi | 4 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). ashkhagan/figshare-brain-tumor-dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-ashkhagan-figshare-brain-tumor-dataset

```bibtex
@misc{scholar4ai-code-kaggle-ashkhagan-figshare-brain-tumor-dataset,
  author = {{Scholar4AI}},
  title = {ashkhagan/figshare-brain-tumor-dataset: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-ashkhagan-figshare-brain-tumor-dataset}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).