A review of Deep learning Techniques for COVID-19 identification on
Chest CT images
- URL: http://arxiv.org/abs/2208.00032v1
- Date: Fri, 29 Jul 2022 18:27:20 GMT
- Title: A review of Deep learning Techniques for COVID-19 identification on
Chest CT images
- Authors: Briskline Kiruba S, Petchiammal A, D. Murugan
- Abstract summary: The current COVID-19 pandemic is a serious threat to humanity that directly affects the lungs.
The standard gold method for diagnosing COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from affected people.
Researchers have proven it to be highly effective for COVID-19 CT image classification.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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.
Related papers
- COVID-19 Disease Identification on Chest-CT images using CNN and VGG16 [0.0]
COVID-19 is an infectious disease caused by a virus originating in Wuhan, China, in December 2019.
In the earlier stage, medical organizations were dazzled because there were no proper health aids or medicine to detect a COVID-19.
This study presents a Convolutional Neural Network (CNN) and VGG16-based model for automated COVID-19 identification on chest CT images.
arXiv Detail & Related papers (2022-07-09T07:20:15Z) - Few-shot Learning for CT Scan based COVID-19 Diagnosis [33.26861533338019]
Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of International Concern infecting more than 40 million people across 188 countries and territories.
Deep learning approaches have become an effective tool for automatic screening of medical images, and it is also being considered for COVID-19 diagnosis.
We propose a supervised domain adaption based COVID-19 CT diagnostic method which can perform effectively when only a small samples of labeled CT scans are available.
arXiv Detail & Related papers (2021-02-01T02:37:49Z) - COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19
from Chest CT Images Through Bigger, More Diverse Learning [70.92379567261304]
We introduce COVID-Net CT-2, enhanced deep neural networks for COVID-19 detection from chest CT images.
We leverage explainability to investigate the decision-making behaviour of COVID-Net CT-2.
Results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment.
arXiv Detail & Related papers (2021-01-19T03:04:09Z) - Screening COVID-19 Based on CT/CXR Images & Building a Publicly
Available CT-scan Dataset of COVID-19 [6.142272540492935]
This study builds a large-size publicly available CT-scan dataset, consisting of more than 13k CT-images of more than 1000 individuals, in which 8k images are taken from 500 patients infected with COVID-19.
We propose a deep learning model for screening COVID-19 using our proposed CT dataset and report the baseline results.
Finally, we extend the proposed CT model for screening COVID-19 from CXR images using a transfer learning approach.
arXiv Detail & Related papers (2020-12-28T11:52:33Z) - COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for
Detection of COVID-19 Cases from Chest CT Images [75.74756992992147]
We introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images.
We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation.
arXiv Detail & Related papers (2020-09-08T15:49:55Z) - A New Screening Method for COVID-19 based on Ocular Feature Recognition
by Machine Learning Tools [66.20818586629278]
Coronavirus disease 2019 (COVID-19) has affected several million people.
New screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19.
arXiv Detail & Related papers (2020-09-04T00:50:27Z) - A comparison of deep machine learning algorithms in COVID-19 disease
diagnosis [4.636229382827605]
The aim of the work is to use deep neural network models for solving the problem of image recognition.
In this work, x-ray images are used for the diagnosis of suspected COVID-19 patients using modern machine learning techniques.
arXiv Detail & Related papers (2020-08-25T10:51:54Z) - COVID-19 CT Image Synthesis with a Conditional Generative Adversarial
Network [26.12568967493797]
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019.
Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis.
Deep-learning-based computer vision methods have demonstrated great promise for use in medical imaging applications.
arXiv Detail & Related papers (2020-07-29T07:20:06Z) - Synergistic Learning of Lung Lobe Segmentation and Hierarchical
Multi-Instance Classification for Automated Severity Assessment of COVID-19
in CT Images [61.862364277007934]
We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images.
A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients.
Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
arXiv Detail & Related papers (2020-05-08T03:16:15Z) - COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19 [92.4955073477381]
The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe.
Deep learning has been used recently as effective computer-aided means to improve diagnostic efficiency.
We propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA.
arXiv Detail & Related papers (2020-04-30T03:13:40Z) - Residual Attention U-Net for Automated Multi-Class Segmentation of
COVID-19 Chest CT Images [46.844349956057776]
coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy.
There is still lack of studies on effectively quantifying the lung infection caused by COVID-19.
We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
arXiv Detail & Related papers (2020-04-12T16:24:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.