---
title: "aptos2019-blindness-detection: papers citing this dataset"
description: "19 papers in the tracked journals link to the Kaggle dataset aptos2019-blindness-detection in their Crossref records, the first in 2022; most of them (6) in 2023."
url: https://scholar4ai.com/code/kaggle-competition-aptos2019-blindness-detection
markdown_url: https://scholar4ai.com/code/kaggle-competition-aptos2019-blindness-detection.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "19 papers in the tracked journals link to the Kaggle dataset aptos2019-blindness-detection in their Crossref records, the first in 2022; most of them (6) 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)
---

# aptos2019-blindness-detection: papers linking to it

Research papers in the 480 tracked journals that link to aptos2019-blindness-detection (https://www.kaggle.com/competitions/aptos2019-blindness-detection), 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/competitions/aptos2019-blindness-detection

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

## Key findings

- 19 papers in the tracked journals link to the Kaggle dataset aptos2019-blindness-detection in their Crossref records, the first in 2022; most of them (6) in 2023.
- By category: AI (19); the journals with the most are Expert Systems with Applications (5), Neural Computing and Applications (4) and Knowledge-Based Systems (2).
- 4 of the papers build on one another: a later paper cites an earlier one that also links aptos2019-blindness-detection.

## By year

*Papers linking to aptos2019-blindness-detection by publication year*

| Year | Papers |
|---|---:|
| 2022 | 2 |
| 2023 | 6 |
| 2024 | 2 |
| 2025 | 5 |
| 2026 | 4 |

## Journals

*Journals whose papers link to aptos2019-blindness-detection most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 5 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 4 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2 |
| [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2 |
| [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 2 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier](https://doi.org/10.1016/j.eswa.2023.119557) (Expert Systems with Applications, 2023)
  - [Two-stage framework for diabetic retinopathy diagnosis and disease stage screening with ensemble learning](https://doi.org/10.1016/j.eswa.2023.120206) (Expert Systems with Applications, 2023) — cites the paper above
- [Automated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanism](https://doi.org/10.1007/s00521-023-09001-1) (Neural Computing and Applications, 2023)
  - [DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network](https://doi.org/10.1186/s40537-024-00959-w) (Journal Of Big Data, 2024) — cites the paper above

## Papers

*Papers linking to aptos2019-blindness-detection, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-01-13 | [Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier](https://doi.org/10.1016/j.eswa.2023.119557) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Md. Nahiduzzaman, Md. Robiul Islam, Md. Omaer Faruq Goni et al. | 142 | cites it |
| 2023-12-19 | [CTNet: convolutional transformer network for diabetic retinopathy classification](https://doi.org/10.1007/s00521-023-09304-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ruchika Bala, Arun Sharma, Nidhi Goel | 36 | cites it |
| 2025-05-09 | [Enhanced multi-grade diabetic retinopathy detection and classification via ensembled deep learning model from retinal fundus images](https://doi.org/10.1016/j.eswa.2025.128116) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Peddapullaiahgari Hariobulesu, Fahimuddin Shaik | 30 | cites it |
| 2023-04-20 | [Two-stage framework for diabetic retinopathy diagnosis and disease stage screening with ensemble learning](https://doi.org/10.1016/j.eswa.2023.120206) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Mohammad H. Alshayeji, Sa’ed Abed, Silpa ChandraBhasi Sindhu | 23 | cites it |
| 2022-12-16 | [Joint grading of diabetic retinopathy and diabetic macular edema using an adaptive attention block and semisupervised learning](https://doi.org/10.1007/s10489-022-04295-5) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Xiaoxin Guo, Xiang Li, Qifeng Lin et al. | 22 | cites it |
| 2023-09-11 | [Automated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanism](https://doi.org/10.1007/s00521-023-09001-1) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Maryam Dinpajhouh, Seyyed Ali Seyyedsalehi | 22 | cites it |
| 2022-11-05 | [Diabetic retinopathy classification based on dense connectivity and asymmetric convolutional neural network](https://doi.org/10.1007/s00521-022-07952-5) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Juan Cao, Jiaran Chen, Xinying Zhang et al. | 13 | cites it |
| 2023-06-28 | [AO-HRCNN: archimedes optimization and hybrid region-based convolutional neural network for detection and classification of diabetic retinopathy](https://doi.org/10.1007/s10462-023-10516-1) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Sujatha Krishnamoorthy, yu Weifeng, Luo Jin et al. | 11 | cites it |
| 2024-08-03 | [DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network](https://doi.org/10.1186/s40537-024-00959-w) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Md Nuho Ul Alam, Ibrahim Hasnine, Erfanul Hoque Bahadur et al. | 9 | cites it |
| 2025-12-20 | [DualAttendMed: A coarse-to-fine dual-stage attention framework for interpretable disease localization and classification](https://doi.org/10.1016/j.eswa.2025.130886) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Junaid Abbas, Danyal Badar Soomro, Shanshan Huang et al. | 9 | cites it |
| 2023-09-13 | [ESOA-HGRU: egret swarm optimization algorithm-based hybrid gated recurrent unit for classification of diabetic retinopathy](https://doi.org/10.1007/s10462-023-10532-1) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Abrar Mohammed Alajlan, Abdul Razaque | 8 | cites it |
| 2024-05-06 | [Pathologic myopia diagnosis and localization from retinal fundus images using custom CNN](https://doi.org/10.1007/s00521-024-09851-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Pammi Kumari, Priyank Saxena | 6 | cites it |
| 2025-07-07 | [Bi-branch bidirectional coupled interaction fusion network for multi-retinal diseases diagnosis](https://doi.org/10.1016/j.knosys.2025.114069) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Shaobin Chen, Tao Tan, Xinyu Zhao et al. | 2 | cites it |
| 2026-04-30 | [RobustDRNet: A clinically-aligned hybrid ensemble model with multi-method explainability for lesion-aware diabetic retinopathy grading](https://doi.org/10.1016/j.eswa.2026.132615) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Pir Bakhsh Khokhar, Viviana Pentangelo, Carmine Gravino et al. | 2 | cites it |
| 2025-05-13 | [OBoctNet: Enhancing Ophthalmic Biomarker Detection Through Active Learning and Explainable AI in Radiological Analysis](https://doi.org/10.1007/s12559-025-10451-z) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Samya Acharja, Md. Tanvir Rahman, Farjana Haque Chamok et al. | 1 | cites it |
| 2026-04-01 | [Towards reliable medical unsupervised anomaly detection: A benchmark and dataset for PET/CT with cross-modality knowledge distillation network](https://doi.org/10.1016/j.inffus.2026.104341) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Muhao Xu, Lili Qu, Zihan Nie et al. | 1 | cites it |
| 2026-04-15 | [A large-scale multi-view fundus images dataset and benchmark for diabetic retinopathy grading](https://doi.org/10.1016/j.inffus.2026.104363) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Xiaoling Luo, Qiaojian Zheng, Chengliang Liu et al. | 1 | cites it |
| 2025-10-06 | [Analysis of model-based ensemble learning for accurate diabetic retinopathy grading](https://doi.org/10.1016/j.knosys.2025.114581) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Huang-Chia Shih, Yuchang Cheng, Chia-Shiang Chen et al. | 0 | cites it |
| 2026-07-21 | [MRM++: Enhanced Masked Relation Modeling for Multi-Modal Medical Pre-training](https://doi.org/10.1007/s11263-026-02938-5) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Qiushi Yang, Wuyang Li, Zhe Peng et al. | 0 | 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). aptos2019-blindness-detection: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-aptos2019-blindness-detection

```bibtex
@misc{scholar4ai-code-kaggle-competition-aptos2019-blindness-detection,
  author = {{Scholar4AI}},
  title = {aptos2019-blindness-detection: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-aptos2019-blindness-detection}},
  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/).