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

TL;DR

This paper reviews recent deep learning methods for detecting COVID-19 from chest CT images, highlighting their effectiveness and comparing various models to aid rapid diagnosis.

Contribution

It provides a comprehensive overview of deep learning techniques applied to COVID-19 CT image classification, summarizing recent research and comparing model performances.

Findings

Deep learning models show high accuracy in COVID-19 detection from CT images

Comparison of different models reveals varying effectiveness and efficiency

CT image analysis with deep learning can support faster diagnosis

Abstract

The current COVID-19 pandemic is a serious threat to humanity that directly affects the lungs. Automatic identification of COVID-19 is a challenge for health care officials. The standard gold method for diagnosing COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from affected people. Some limitations encountered while collecting swabs are related to accuracy and longtime duration. Chest CT (Computed Tomography) is another test method that helps healthcare providers quickly identify the infected lung areas. It was used as a supporting tool for identifying COVID-19 in an earlier stage. With the help of deep learning, the CT imaging characteristics of COVID-19. Researchers have proven it to be highly effective for COVID-19 CT image classification. In this study, we review the recent deep learning techniques that can use to detect the COVID-19 disease. Relevant studies were collected by various databases such as Web of Science, Google Scholar, and PubMed. Finally, we compare the results of different deep learning models, and CT image analysis is discussed.