# A new composite approach for COVID-19 detection in X-ray images using deep features

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-new-composite-approach-for-covid-19-detection-in-x-ray-images-using-deep/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Tayyip Özcan |
| Citations | 25 |
| DOI | 10.1016/j.asoc.2021.107669 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | green |
| OA URL | https://www.ncbi.nlm.nih.gov/pmc/articles/8255192 |
| OpenAlex ID | https://openalex.org/W3182814038 |
| PMID | 34248447 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Tayyip Özcan](https://scholariq.org/researchers/tayyip-ozcan/)

## Paper primary topic

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)

## Paper topics

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)
- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.