---
title: "andrewmvd/convid19-x-rays: papers citing this dataset"
description: "6 papers in the tracked journals link to the Kaggle dataset andrewmvd/convid19-x-rays in their Crossref records, the first in 2020; most of them (3) in 2021."
url: https://scholar4ai.com/code/kaggle-andrewmvd-convid19-x-rays
markdown_url: https://scholar4ai.com/code/kaggle-andrewmvd-convid19-x-rays.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "6 papers in the tracked journals link to the Kaggle dataset andrewmvd/convid19-x-rays in their Crossref records, the first in 2020; most of them (3) in 2021. (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)
---

# andrewmvd/convid19-x-rays: papers linking to it

Research papers in the 480 tracked journals that link to andrewmvd/convid19-x-rays (https://www.kaggle.com/datasets/andrewmvd/convid19-x-rays), 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/andrewmvd/convid19-x-rays

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

## Key findings

- 6 papers in the tracked journals link to the Kaggle dataset andrewmvd/convid19-x-rays in their Crossref records, the first in 2020; most of them (3) in 2021.
- By category: AI (6); the journals with the most are Applied Intelligence (3), Neural Computing and Applications (1) and Cognitive Computation (1).

## By year

*Papers linking to andrewmvd/convid19-x-rays by publication year*

| Year | Papers |
|---|---:|
| 2020 | 2 |
| 2021 | 3 |
| 2022 | 1 |

## Journals

*Journals whose papers link to andrewmvd/convid19-x-rays most*

| Journal | Papers |
|---|---:|
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 3 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |

## Papers

*Papers linking to andrewmvd/convid19-x-rays, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 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 |
| 2020-11-27 | [An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images](https://doi.org/10.1007/s10489-020-02010-w) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Swati Hira, Anita Bai, Sanchit Hira | 111 | cites it |
| 2021-05-03 | [Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images](https://doi.org/10.1016/j.eswa.2021.115141) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Adi Alhudhaif, Kemal Polat, Onur Karaman | 98 | cites it |
| 2022-12-28 | [COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans](https://doi.org/10.1007/s12559-022-10076-6) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Kirti Raj Bhatele, Anand Jha, Devanshu Tiwari et al. | 53 | cites it |
| 2021-01-06 | [Intelligent system for COVID-19 prognosis: a state-of-the-art survey](https://doi.org/10.1007/s10489-020-02102-7) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Janmenjoy Nayak, Bighnaraj Naik, Paidi Dinesh et al. | 51 | cites it |
| 2021-04-29 | [A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays](https://doi.org/10.1007/s00521-021-06044-0) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Fouzia Altaf, Syed Mohammed Shamsul Islam, Naeem Khalid Janjua | 37 | 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). andrewmvd/convid19-x-rays: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-andrewmvd-convid19-x-rays

```bibtex
@misc{scholar4ai-code-kaggle-andrewmvd-convid19-x-rays,
  author = {{Scholar4AI}},
  title = {andrewmvd/convid19-x-rays: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-andrewmvd-convid19-x-rays}},
  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/).