---
title: "nih-chest-xrays/data: papers citing this dataset"
description: "5 papers in the tracked journals link to the Kaggle dataset nih-chest-xrays/data in their Crossref records, the first in 2020; most of them (2) in 2023."
url: https://scholar4ai.com/code/kaggle-nih-chest-xrays-data
markdown_url: https://scholar4ai.com/code/kaggle-nih-chest-xrays-data.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 nih-chest-xrays/data in their Crossref records, the first in 2020; most of them (2) in 2023. (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)
---

# nih-chest-xrays/data: papers linking to it

Research papers in the 480 tracked journals that link to nih-chest-xrays/data (https://www.kaggle.com/datasets/nih-chest-xrays/data), 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/nih-chest-xrays/data

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

## Key findings

- 5 papers in the tracked journals link to the Kaggle dataset nih-chest-xrays/data in their Crossref records, the first in 2020; most of them (2) in 2023.
- By category: AI (5); the journals with the most are Expert Systems with Applications (2), Applied Intelligence (1) and Neural Computing and Applications (1).

## By year

*Papers linking to nih-chest-xrays/data by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2023 | 2 |
| 2024 | 1 |
| 2025 | 1 |

## Journals

*Journals whose papers link to nih-chest-xrays/data most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 1 |

## Papers

*Papers linking to nih-chest-xrays/data, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-06-12 | [Iterative enhancement fusion-based cascaded model for detection and localization of multiple disease from CXR-Images](https://doi.org/10.1016/j.eswa.2024.124464) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Satvik Vats, Vikrant Sharma, Karan Singh et al. | 392 | cites it |
| 2020-08-20 | [Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices](https://doi.org/10.1007/s10489-020-01826-w) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Sakshi Ahuja, Bijaya Ketan Panigrahi, Nilanjan Dey et al. | 380 | cites it |
| 2023-04-11 | [A lightweight CORONA-NET for COVID-19 detection in X-ray images](https://doi.org/10.1016/j.eswa.2023.120023) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Muhammad Usman Hadi, Rizwan Qureshi, Ayesha Ahmed et al. | 20 | cites it |
| 2023-02-09 | [ABOA-CNN: auction-based optimization algorithm with convolutional neural network for pulmonary disease prediction](https://doi.org/10.1007/s00521-022-08033-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Balaji Annamalai, S. Prabakeran, Indumathi Varadharajan | 9 | cites it |
| 2025-11-06 | [Emulating human-like adaptive vision for efficient and flexible machine visual perception](https://doi.org/10.1038/s42256-025-01130-7) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Yulin Wang, Yue Yang, Yang Yue et al. | 9 | 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). nih-chest-xrays/data: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-nih-chest-xrays-data

```bibtex
@misc{scholar4ai-code-kaggle-nih-chest-xrays-data,
  author = {{Scholar4AI}},
  title = {nih-chest-xrays/data: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-nih-chest-xrays-data}},
  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/).