Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning

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

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

Frontiers in Medicine ◽

10.3389/fmed.2020.608525 ◽

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.

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

Journal of Computational Vision and Imaging Systems ◽

10.15353/jcvis.v6i1.3547 ◽

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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COVID-19 Diagnosis with Deep Learning

Ingeniería e Investigación ◽

10.15446/ing.investig.v42n1.88825 ◽

2021 ◽

Vol 42 (1) ◽

pp. e88825

Author(s):

Hatice Catal Reis

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Performance Metrics ◽

Binary Classification ◽

Network Models ◽

Ct Images ◽

Chest Ct ◽

Point Of View ◽

Neural Network Models ◽

Convolutional Network

The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images). The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC. Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.

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COVID-19 Diagnosis from CT Images with Convolutional Neural Network Optimized by Marine Predator Optimization Algorithm

BioMed Research International ◽

10.1155/2021/5122962 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Huaping Jia ◽

Junlong Zhao ◽

Ali Arshaghi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Optimization Algorithm ◽

Human Life ◽

Optimization Method ◽

Ct Images ◽

Chest Ct ◽

Public Health Systems ◽

Marine Predator ◽

The World

In recent years, almost every country in the world has struggled against the spread of Coronavirus Disease 2019. If governments and public health systems do not take action against the spread of the disease, it will have a severe impact on human life. A noteworthy technique to stop this pandemic is diagnosing COVID-19 infected patients and isolating them instantly. The present study proposes a method for the diagnosis of COVID-19 from CT images. The method is a hybrid method based on convolutional neural network which is optimized by a newly introduced metaheuristic, called marine predator optimization algorithm. This optimization method is performed to improve the system accuracy. The method is then implemented on the chest CT scans with the COVID-19-related findings (MosMedData) dataset, and the results are compared with three other methods from the literature to indicate the method’s performance. The final results indicate that the proposed method with 98.11% accuracy, 98.13% precision, 98.66% sensitivity, and 97.26% F 1 score has the highest performance in all indicators than the compared methods which shows its higher accuracy and reliability.

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COVID-19 Detection from Chest CT Images using Ensemble Deep Convolutional Neural Network

2021 2nd International Conference for Emerging Technology (INCET) ◽

10.1109/incet51464.2021.9456387 ◽

2021 ◽

Author(s):

Md. Foysal ◽

A. B. M. Aowlad Hossain

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Ct Images ◽

Chest Ct ◽

Deep Convolutional Neural Network

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Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method

BMC Bioinformatics ◽

10.1186/s12859-021-04083-x ◽

2021 ◽

Vol 22 (S5) ◽

Author(s):

Yao-Mei Chen ◽

Yenming J. Chen ◽

Wen-Hsien Ho ◽

Jinn-Tsong Tsai

Keyword(s):

Neural Network ◽

Experimental Design ◽

Convolutional Neural Network ◽

Design Method ◽

Performance Testing ◽

Ct Images ◽

Chest Ct ◽

Majority Voting ◽

Ensemble Model ◽

Neural Network Ensemble

Abstract Background To classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images. Results A convolutional neural network (CNN) ensemble model was developed for classifying chest CT images as positive or negative for COVID-19. To classify chest CT images acquired from COVID-19 patients, the proposed COVID19-CNN ensemble model combines the use of multiple trained CNN models with a majority voting strategy. The CNN models were trained to classify chest CT images by transfer learning from well-known pre-trained CNN models and by applying their algorithm hyperparameters as appropriate. The combination of algorithm hyperparameters for a pre-trained CNN model was determined by uniform experimental design. The chest CT images (405 from COVID-19 patients and 397 from healthy patients) used for training and performance testing of the COVID19-CNN ensemble model were obtained from an earlier study by Hu in 2020. Experiments showed that, the COVID19-CNN ensemble model achieved 96.7% accuracy in classifying CT images as COVID-19 positive or negative, which was superior to the accuracies obtained by the individual trained CNN models. Other performance measures (i.e., precision, recall, specificity, and F1-score) obtained bythe COVID19-CNN ensemble model were higher than those obtained by individual trained CNN models. Conclusions The COVID19-CNN ensemble model had superior accuracy and excellent capability in classifying chest CT images as COVID-19 positive or negative.

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