---
title: "ieee8023/covid-chestxray-dataset: papers citing it"
description: "36 papers in the tracked journals link to the GitHub repository ieee8023/covid-chestxray-dataset in their Crossref records, the first in 2020; most of them (16) in 2021."
url: https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset
markdown_url: https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "36 papers in the tracked journals link to the GitHub repository ieee8023/covid-chestxray-dataset in their Crossref records, the first in 2020; most of them (16) 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)
---

# ieee8023/covid-chestxray-dataset: papers linking to it

Research papers in the 480 tracked journals that link to ieee8023/covid-chestxray-dataset (https://github.com/ieee8023/covid-chestxray-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.

- GitHub
- 3,064 stars
- 1,275 forks
- Jupyter Notebook
- created 2020
- https://github.com/ieee8023/covid-chestxray-dataset

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

## Key findings

- 36 papers in the tracked journals link to the GitHub repository ieee8023/covid-chestxray-dataset in their Crossref records, the first in 2020; most of them (16) in 2021.
- By category: AI (32) and Information Systems (4); the journals with the most are Expert Systems with Applications (8), Applied Intelligence (7) and Neural Computing and Applications (6).
- 16 of the papers build on one another: a later paper cites an earlier one that also links ieee8023/covid-chestxray-dataset.

## By year

*Papers linking to ieee8023/covid-chestxray-dataset by publication year*

| Year | Papers |
|---|---:|
| 2020 | 6 |
| 2021 | 16 |
| 2022 | 6 |
| 2023 | 5 |
| 2024 | 2 |
| 2025 | 1 |

## Journals

*Journals whose papers link to ieee8023/covid-chestxray-dataset most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 8 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 7 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 6 |
| [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 5 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 3 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble](https://doi.org/10.1016/j.eswa.2020.113909) (Expert Systems with Applications, 2020)
  - [Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19](https://doi.org/10.1016/j.eswa.2023.120528) (Expert Systems with Applications, 2023) — cites the paper above
- [Deep learning approaches for COVID-19 detection based on chest X-ray images](https://doi.org/10.1016/j.eswa.2020.114054) (Expert Systems with Applications, 2020)
  - [A Review Study of the Deep Learning Techniques used for the Classification of Chest Radiological Images for COVID-19 Diagnosis](https://doi.org/10.1016/j.jjimei.2022.100100) (International Journal of Information Management Data Insights, 2022) — cites the paper above
  - [An integrated convolutional neural network with attention guidance for improved performance of medical image classification](https://doi.org/10.1007/s00521-023-09164-x) (Neural Computing and Applications, 2023) — cites the paper above
  - [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, 2024) — cites the paper above
- [MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients](https://doi.org/10.1016/j.patcog.2020.107700) (Pattern Recognition, 2020)
  - [Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images](https://doi.org/10.1016/j.patcog.2022.108826) (Pattern Recognition, 2022) — cites the paper above
- [Intelligent system for COVID-19 prognosis: a state-of-the-art survey](https://doi.org/10.1007/s10489-020-02102-7) (Applied Intelligence, 2021)
  - [The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic](https://doi.org/10.1007/s10796-021-10131-x) (Information Systems Frontiers, 2021) — cites the paper above
- [COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier](https://doi.org/10.1007/s12559-021-09848-3) (Cognitive Computation, 2021)
  - [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, 2021) — cites the paper above
- [AI for radiographic COVID-19 detection selects shortcuts over signal](https://doi.org/10.1038/s42256-021-00338-7) (Nature Machine Intelligence, 2021)
  - [Explainable AI reveals Clever Hans effects in unsupervised learning models](https://doi.org/10.1038/s42256-025-01000-2) (Nature Machine Intelligence, 2025) — cites the paper above
- [Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost](https://doi.org/10.1016/j.eswa.2021.115452) (Expert Systems with Applications, 2021)
  - [RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images](https://doi.org/10.1016/j.eswa.2022.117410) (Expert Systems with Applications, 2022) — cites the paper above

## Papers

*Papers linking to ieee8023/covid-chestxray-dataset, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-09-28 | [Deep learning approaches for COVID-19 detection based on chest X-ray images](https://doi.org/10.1016/j.eswa.2020.114054) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Aras Masood Ismael, Abdulkadir Şengür | 748 | cites it |
| 2021-05-31 | [AI for radiographic COVID-19 detection selects shortcuts over signal](https://doi.org/10.1038/s42256-021-00338-7) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Alex J. DeGrave, Joseph D. Janizek, Su‐In Lee | 631 | cites it |
| 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 |
| 2020-08-26 | [Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble](https://doi.org/10.1016/j.eswa.2020.113909) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Tej Bahadur Chandra, Kesari Verma, Bikesh Kumar Singh et al. | 278 | cites it |
| 2020-10-17 | [MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients](https://doi.org/10.1016/j.patcog.2020.107700) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Mohammad Shorfuzzaman, M. Shamim Hossain | 217 | cites it |
| 2021-04-26 | [The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic](https://doi.org/10.1007/s10796-021-10131-x) | [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | Francesco Piccialli, Vincenzo Schiano Di Cola, Fabio Giampaolo et al. | 143 | 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 |
| 2021-06-15 | [FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia](https://doi.org/10.1007/s10796-021-10144-6) | [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | Longling Zhang, Bochen Shen, Ahmed Barnawi et al. | 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 |
| 2021-03-04 | [COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier](https://doi.org/10.1007/s12559-021-09848-3) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Asu Kumar Singh, Anupam Kumar, Mufti Mahmud et al. | 96 | cites it |
| 2022-01-05 | [Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays](https://doi.org/10.1007/s00521-021-06737-6) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ashis Paul, Arpan Basu, Mufti Mahmud et al. | 82 | cites it |
| 2021-09-10 | [A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing](https://doi.org/10.1007/s00521-021-06396-7) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Nidal Nasser, Emad-ul-Haq Qazi, Muhammad Ali Imran et al. | 75 | cites it |
| 2021-02-15 | [Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19](https://doi.org/10.1016/j.eswa.2021.114677) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Plácido L. Vidal, Joaquim de Moura, Jorge Novo et al. | 74 | cites it |
| 2023-05-27 | [Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19](https://doi.org/10.1016/j.eswa.2023.120528) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Md. Nahiduzzaman, Md. Omaer Faruq Goni, Rakibul Hassan et al. | 74 | cites it |
| 2021-06-22 | [Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost](https://doi.org/10.1016/j.eswa.2021.115452) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Domingos Dias, Luana Batista da Cruz, João Otávio Bandeira Diniz et al. | 69 | cites it |
| 2021-02-02 | [Corona-Nidaan: lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detection](https://doi.org/10.1007/s10489-020-01978-9) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Mainak Chakraborty, Sunita Dhavale, Jitendra Ingole | 56 | cites it |
| 2022-06-06 | [Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images](https://doi.org/10.1016/j.patcog.2022.108826) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Ajay Sharma, Pramod Kumar Mishra | 56 | 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 |
| 2021-03-02 | [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 |
| 2021-01-23 | [A new clustering method for the diagnosis of CoVID19 using medical images](https://doi.org/10.1007/s10489-020-02122-3) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Himanshu Mittal, Avinash Chandra Pandey, Raju Pal et al. | 44 | cites it |
| 2022-04-27 | [RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images](https://doi.org/10.1016/j.eswa.2022.117410) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | El‐Sayed A. El‐Dahshan, Mahmoud M. Bassiouni, Ahmed Hagag et al. | 42 | 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 |
| 2025-03-17 | [Explainable AI reveals Clever Hans effects in unsupervised learning models](https://doi.org/10.1038/s42256-025-01000-2) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Jacob Kauffmann, Jonas Dippel, Lukas Ruff et al. | 35 | cites it |
| 2020-11-04 | [A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks](https://doi.org/10.1007/s10489-020-02019-1) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Hüseyin Yaşar, Murat Ceylan | 33 | cites it |
| 2021-11-25 | [Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images](https://doi.org/10.1016/j.patcog.2021.108452) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Haigen Hu, Leizhao Shen, Qiu Guan et al. | 32 | cites it |
| 2023-02-20 | [Evaluation of EfficientNet models for COVID-19 detection using lung parenchyma](https://doi.org/10.1007/s00521-023-08344-z) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Zühal Kurt, Şahin Işık, Zeynep Kaya et al. | 31 | cites it |
| 2023-04-21 | [Explanatory Interactive Machine Learning](https://doi.org/10.1007/s12599-023-00806-x) | [Business & Information Systems Engineering](https://scholar4ai.com/journals/business-information-systems-engineering.md) | Nicolas Pfeuffer, Lorenz Baum, Wolfgang Stammer et al. | 28 | cites it |
| 2023-06-14 | [Capsule networks for computer vision applications: a comprehensive review](https://doi.org/10.1007/s10489-023-04620-6) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Seema Choudhary, Sumeet Saurav, Ravi Saini et al. | 23 | cites it |
| 2023-11-20 | [An integrated convolutional neural network with attention guidance for improved performance of medical image classification](https://doi.org/10.1007/s00521-023-09164-x) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Coşku Öksüz, Oğuzhan Urhan, M. Kemal Güllü | 23 | cites it |
| 2021-12-16 | [An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty](https://doi.org/10.1007/s12559-021-09978-8) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Tawsin Uddin Ahmed, Mohammad Newaj Jamil, Mohammad Shahadat Hossain et al. | 21 | cites it |
| 2022-07-19 | [A Review Study of the Deep Learning Techniques used for the Classification of Chest Radiological Images for COVID-19 Diagnosis](https://doi.org/10.1016/j.jjimei.2022.100100) | [International Journal of Information Management Data Insights](https://scholar4ai.com/journals/international-journal-of-information-management-data-insights.md) | Yu Wang, Carol Anne Hargreaves | 17 | cites it |
| 2022-05-17 | [Joint design and compression of convolutional neural networks as a Bi-level optimization problem](https://doi.org/10.1007/s00521-022-07331-0) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Hassen Louati, Slim Bechikh, Ali Louati et al. | 16 | cites it |
| 2021-07-15 | [Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images](https://doi.org/10.1007/s12559-021-09915-9) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Hüseyin Yaşar, Murat Ceylan | 15 | cites it |
| 2024-10-24 | [SoftLungX: leveraging transfer learning with convolutional neural networks for accurate respiratory disease classification in chest X-ray images](https://doi.org/10.1186/s40537-024-01018-0) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Tijana Geroski, Ognjen Pavić, Lazar Dašić et al. | 5 | 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). ieee8023/covid-chestxray-dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-ieee8023-covid-chestxray-dataset

```bibtex
@misc{scholar4ai-code-github-ieee8023-covid-chestxray-dataset,
  author = {{Scholar4AI}},
  title = {ieee8023/covid-chestxray-dataset: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-ieee8023-covid-chestxray-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/).