---
title: "ucsd-ai4h/covid-ct: papers citing this GitHub repository"
description: "5 papers in the tracked journals link to the GitHub repository ucsd-ai4h/covid-ct in their Crossref records, the first in 2020; most of them (2) in 2021."
url: https://scholar4ai.com/code/github-ucsd-ai4h-covid-ct
markdown_url: https://scholar4ai.com/code/github-ucsd-ai4h-covid-ct.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 GitHub repository ucsd-ai4h/covid-ct in their Crossref records, the first in 2020; most of them (2) 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)
---

# ucsd-ai4h/covid-ct: papers linking to it

Research papers in the 480 tracked journals that link to ucsd-ai4h/covid-ct (https://github.com/ucsd-ai4h/covid-ct), 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.

- GitHub
- https://github.com/ucsd-ai4h/covid-ct

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

## Key findings

- 5 papers in the tracked journals link to the GitHub repository ucsd-ai4h/covid-ct in their Crossref records, the first in 2020; most of them (2) in 2021.
- By category: AI (5); the journals with the most are Expert Systems with Applications (1), Applied Intelligence (1) and Cognitive Computation (1).

## By year

*Papers linking to ucsd-ai4h/covid-ct by publication year*

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

## Journals

*Journals whose papers link to ucsd-ai4h/covid-ct most*

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

## Papers

*Papers linking to ucsd-ai4h/covid-ct, 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 |
| 2020-07-18 | [A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier](https://doi.org/10.1016/j.knosys.2020.106270) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Warda M. Shaban, Asmaa H. Rabie, Ahmed I. Saleh et al. | 227 | cites it |
| 2021-01-19 | [Efficient deep learning approach for augmented detection of Coronavirus disease](https://doi.org/10.1007/s00521-020-05410-8) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ahmed Sedik, Mohamed Adel Hammad, Fathi E. Abd El‐Samie et al. | 188 | cites it |
| 2021-03-02 | [Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis](https://doi.org/10.1007/s12559-020-09779-5) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Sejuti Rahman, Sujan Chandra Sarker, Md Abdullah Al Miraj et al. | 48 | cites it |
| 2022-02-24 | [A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network](https://doi.org/10.1016/j.eswa.2022.116740) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Saeed Sani, Hossein Ebrahimzadeh Shermeh | 15 | 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). ucsd-ai4h/covid-ct: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-ucsd-ai4h-covid-ct

```bibtex
@misc{scholar4ai-code-github-ucsd-ai4h-covid-ct,
  author = {{Scholar4AI}},
  title = {ucsd-ai4h/covid-ct: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-ucsd-ai4h-covid-ct}},
  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/).