# A review of Deep learning Techniques for COVID-19 identification on Chest CT images

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-review-of-deep-learning-techniques-for-covid-19-identification-on-chest-ct/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Briskline Kiruba S,A Petchiammal,D. Murugan |
| Citations | 1 |
| DOI | 10.48550/arxiv.2208.00032 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2208.00032 |
| OpenAlex ID | https://openalex.org/W4289645279 |
| Type | preprint |
| Year | 2022 |

## Paper authors

- [A Petchiammal](https://scholariq.org/researchers/a-petchiammal/)
- [D. Murugan](https://scholariq.org/researchers/d-murugan/)

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## 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/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [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.