# An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/an-optimized-deep-learning-architecture-for-the-diagnosis-of-covid-19-disease/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Dalia Ezzat,Aboul Ella Hassanien,Hassan Aboul-Ella |
| Citations | 213 |
| DOI | 10.1016/j.asoc.2020.106742 |
| Fields | Computer Science,Medicine,Neuroscience |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2004.05084 |
| OpenAlex ID | https://openalex.org/W3015538848 |
| PMID | 32982615 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Dalia Ezzat](https://scholariq.org/researchers/dalia-ezzat/)

## 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/)
- [Brain Tumor Detection and Classification](https://scholariq.org/topics/brain-tumor-detection-and-classification/)

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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.