scholarly journals Identification of COVID - 19 from Chest CT Images using a Deep Neural Network with SVM Classification

2021 ◽  
Vol 1916 ◽  
pp. 012064
Author(s):  
B Akshitha ◽  
M Arthi ◽  
R Brindha ◽  
G Sandhya
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Ct Images ◽  
Chest Ct ◽  
Svm Classification

scholarly journals Online COVID-19 diagnosis with chest CT images: Lesion-attention deep neural networks

2020 ◽  
Author(s):  
Bin Liu ◽  
Xiaoxue Gao ◽  
Mengshuang He ◽  
Fengmao Lv ◽  
Guosheng Yin
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Area Under The Curve ◽  
Ct Images ◽  
Chest Ct ◽  
Diagnostic Tools ◽  
Ct Image ◽  
Medical Systems ◽  
Negative Case

Chest computed tomography (CT) scanning is one of the most important technologies for COVID-19 diagnosis and disease monitoring, particularly for early detection of coronavirus. Recent advancements in computer vision motivate more concerted efforts in developing AI-driven diagnostic tools to accommodate the enormous demands for the COVID-19 diagnostic tests globally. To help alleviate burdens on medical systems, we develop a lesion-attention deep neural network (LA-DNN) to predict COVID-19 positive or negative with a richly annotated chest CT image dataset. Based on the textual radiological report accompanied with each CT image, we extract two types of important information for the annotations: One is the indicator of a positive or negative case of COVID-19, and the other is the description of five lesions on the CT images associated with the positive cases. The proposed data-efficient LA-DNN model focuses on the primary task of binary classification for COVID-19 diagnosis, while an auxiliary multi-label learning task is implemented simultaneously to draw the model's attention to the five lesions associated with COVID-19. The joint task learning process makes it a highly sample-efficient deep neural network that can learn COVID-19 radiology features more effectively with limited but high-quality, rich-information samples. The experimental results show that the area under the curve (AUC) and sensitivity (recall), precision, and accuracy for COVID-19 diagnosis are 94.0%, 88.8%, 87.9%, and 88.6% respectively, which reach the clinical standards for practical use. A free online system is currently alive for fast diagnosis using CT images at the website https://www.covidct.cn/, and all codes and datasets are freely accessible at our github address.

scholarly journals B-PO04-180 LEFT ATRIUM RIGHT ATRIUM NETWORK (LARANET): A DEEP NEURAL NETWORK FOR BIATRIAL SEGMENTATION FROM MRI AND CT IMAGES

Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S352
Author(s):  
Rebecca Yu ◽  
Rheeda L. Ali ◽  
Pallavi Pandey ◽  
Ryan P. Bradley ◽  
David D. Spragg ◽  
...  
Keyword(s):  
Neural Network ◽  
Left Atrium ◽  
Right Atrium ◽  
Deep Neural Network ◽  
Ct Images

An optimized two-stage cascaded deep neural network for adrenal segmentation on CT images

2021 ◽  
pp. 104749
Author(s):  
Guoting Luo ◽  
Qing Yang ◽  
Tao Chen ◽  
Tao Zheng ◽  
Wei Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Ct Images ◽  
Two Stage

scholarly journals Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

2021 ◽  
Vol 18 (11) ◽  
Author(s):  
Houssam BENBRAHIM ◽  
Hanaa HACHIMI ◽  
Aouatif AMINE
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Virus Disease ◽  
Ct Images ◽  
Chest Ct ◽  
Apache Spark ◽  
Difficult Situation ◽  
Novel Coronavirus

The SARS-CoV-2 (COVID-19) has propagated rapidly around the world, and it became a global pandemic. It has generated a catastrophic effect on public health. Thus, it is crucial to discover positive cases as early as possible to treat touched patients fastly. Chest CT is one of the methods that play a significant role in diagnosing 2019-nCoV acute respiratory disease. The implementation of advanced deep learning techniques combined with radiological imaging can be helpful for the precise detection of the novel coronavirus. It can also be assistive to surmount the difficult situation of the lack of medical skills and specialized doctors in remote regions. This paper presented Deep Transfer Learning Pipelines with Apache Spark and KerasTensorFlow combined with the Logistic Regression algorithm for automatic COVID-19 detection in chest CT images, using Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception. Our model produced a classification accuracy of 85.64, 84.25, and 82.87 %, respectively, for VGG16, VGG19, and Xception. HIGHLIGHTS Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression using CT images to screen for Corona Virus Disease (COVID-19)       Automatic detection of  COVID-19 in chest CT images Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception to predict COVID-19 in Computed Tomography image GRAPHICAL ABSTRACT

scholarly journals COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images

2020 ◽  
Vol 7 ◽  
Author(s):  
Hayden Gunraj ◽  
Linda Wang ◽  
Alexander Wong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Ct Images ◽  
Ct Imaging ◽  
Chest Ct ◽  
Deep Convolutional Neural Network ◽  
Screening Tools ◽  
Screening Methods ◽  
Rt Pcr

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients have worsening respiratory status or developing complications that require expedited care, or patients are suspected to be COVID-19-positive but have negative RT-PCR test results. Early studies on CT-based screening have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, in this study we introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach. Additionally, we introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation comprising 104,009 images across 1,489 patient cases. Furthermore, in the interest of reliability and transparency, we leverage an explainability-driven performance validation strategy to investigate the decision-making behavior of COVIDNet-CT, and in doing so ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images. Both COVIDNet-CT and the COVIDx-CT dataset are available to the general public in an open-source and open access manner as part of the COVID-Net initiative. While COVIDNet-CT is not yet a production-ready screening solution, we hope that releasing the model and dataset will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

scholarly journals COVIDetect (A Deep Neural Network for Detecting COVID-19 Cases from Chest CT-scan Images)

2021 ◽  
Vol 2 (21) ◽  
pp. 65-77
Author(s):  
Md. Mobarak Hossain ◽  
◽  
Muhammad Usama Islam ◽  
Dr. Mohammod Abul Kashem
Keyword(s):  
Neural Network ◽  
Ct Scan ◽  
Deep Neural Network ◽  
Human Life ◽  
Chest Ct ◽  
Chest Ct Scan

Coronavirus (COVID) has claimed numerous lives since its outbreak in late 2019. It is estimated that around 72 million people are affected by the virus and a toll on human life has reached 1.6 millions as of December 2020 making it one of the worst pandemic in recorded history.

scholarly journals COVIDNet-CT: Detection of COVID-19 from Chest CT Images using a Tailored Deep Convolutional Neural Network Architecture

2021 ◽  
Vol 6 (1) ◽  
pp. 1-3
Author(s):  
Hayden Gunraj ◽  
Linda Wang ◽  
Alexander Wong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Screening Method ◽  
Ct Images ◽  
Chest Ct ◽  
Deep Convolutional Neural Network ◽  
Screening Tools ◽  
Neural Network Architecture ◽  
Tremendous Impact

The COVID-19 pandemic continues to have a tremendous impact on patients and healthcare systems around the world. To combat this disease, there is a need for effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as a key screening method to complement RT-PCR testing. Early studies have reported abnormalities in chest CT images which are characteristic of COVID-19 infection, but these abnormalities may be difficult to distinguish from abnormalities caused by other lung conditions. Motivated by this, we introduce COVIDNet-CT, a deep convolutional neural network architecture tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a CT image dataset comprising 104,009 images across 1,489 patient cases. Finally, we leverage explainability to investigate the decision-making behaviour of COVIDNet-CT and ensure that COVIDNet-CT makes predictions based on relevant indicators in CT images.

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