  Purpose: Early detection and diagnosis of Covid-19 and accurate separation of
patients with non-Covid-19 cases at the lowest cost and in the early stages of
the disease are one of the main challenges in the epidemic of Covid-19.
Concerning the novelty of the disease, the diagnostic methods based on
radiological images suffer shortcomings despite their many uses in diagnostic
centers. Accordingly, medical and computer researchers tended to use
machine-learning models to analyze radiology images.
  Methods: Present systematic review was conducted by searching three databases
of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020
Based on a search strategy, the keywords were Covid-19, Deep learning,
Diagnosis and Detection leading to the extraction of 168 articles that
ultimately, 37 articles were selected as the research population by applying
inclusion and exclusion criteria. Result: This review study provides an
overview of the current state of all models for the detection and diagnosis of
Covid-19 through radiology modalities and their processing based on deep
learning. According to the finding, Deep learning Based models have an
extraordinary capacity to achieve an accurate and efficient system for the
detection and diagnosis of Covid-19, which using of them in the processing of
CT-Scan and X-Ray images, would lead to a significant increase in sensitivity
and specificity values.
  Conclusion: The Application of Deep Learning (DL) in the field of Covid-19
radiologic image processing leads to the reduction of false-positive and
negative errors in the detection and diagnosis of this disease and provides an
optimal opportunity to provide fast, cheap, and safe diagnostic services to
patients.
