---
title: "kmader/skin-cancer-mnist-ham10000: papers citing it"
description: "5 papers in the tracked journals link to the Kaggle dataset kmader/skin-cancer-mnist-ham10000 in their Crossref records, the first in 2023; most of them (2) in 2025."
url: https://scholar4ai.com/code/kaggle-kmader-skin-cancer-mnist-ham10000
markdown_url: https://scholar4ai.com/code/kaggle-kmader-skin-cancer-mnist-ham10000.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "5 papers in the tracked journals link to the Kaggle dataset kmader/skin-cancer-mnist-ham10000 in their Crossref records, the first in 2023; most of them (2) in 2025. (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)
---

# kmader/skin-cancer-mnist-ham10000: papers linking to it

Research papers in the 480 tracked journals that link to kmader/skin-cancer-mnist-ham10000 (https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000), 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/kmader/skin-cancer-mnist-ham10000

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

## Key findings

- 5 papers in the tracked journals link to the Kaggle dataset kmader/skin-cancer-mnist-ham10000 in their Crossref records, the first in 2023; most of them (2) in 2025.
- By category: AI (5); the journals with the most are Knowledge-Based Systems (2), Neural Computing and Applications (2) and Cognitive Computation (1).

## By year

*Papers linking to kmader/skin-cancer-mnist-ham10000 by publication year*

| Year | Papers |
|---|---:|
| 2023 | 2 |
| 2024 | 1 |
| 2025 | 2 |

## Journals

*Journals whose papers link to kmader/skin-cancer-mnist-ham10000 most*

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

## Papers

*Papers linking to kmader/skin-cancer-mnist-ham10000, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-05-19 | [EHR privacy preservation using federated learning with DQRE-Scnet for healthcare application domains](https://doi.org/10.1016/j.knosys.2023.110638) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | C. U. Om Kumar, Sudhakaran Gajendran, R.M. Bhavadharini et al. | 34 | cites it |
| 2023-08-19 | [A novel uncertainty-aware deep learning technique with an application on skin cancer diagnosis](https://doi.org/10.1007/s00521-023-08930-1) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Afshar Shamsi, Hamzeh Asgharnezhad, Ziba Bouchani et al. | 25 | cites it |
| 2024-12-31 | [A dynamic ensemble learning based data mining framework for medical imbalanced big data](https://doi.org/10.1016/j.knosys.2024.112947) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Rithani Mohan, R Prasanna Kumar, Altalbe Ali | 11 | cites it |
| 2025-06-05 | [Application of ensemble learning models in computer-aided diagnosis of skin diseases](https://doi.org/10.1007/s00521-025-11336-w) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Abu S. Asaduzzaman, Christian C. Thompson, Fadi N. Sibai et al. | 10 | cites it |
| 2025-05-13 | [Enhancing Skin Disease Diagnosis: Light GBM-DMS Algorithm for Accurate Image Classification](https://doi.org/10.1007/s12559-025-10450-0) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | B. Kalpana, A. Senthilselvi, S. Muruganandam et al. | 2 | 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). kmader/skin-cancer-mnist-ham10000: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-kmader-skin-cancer-mnist-ham10000

```bibtex
@misc{scholar4ai-code-kaggle-kmader-skin-cancer-mnist-ham10000,
  author = {{Scholar4AI}},
  title = {kmader/skin-cancer-mnist-ham10000: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-kmader-skin-cancer-mnist-ham10000}},
  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/).