A seven-layer convolutional neural network for chest CT based COVID-19 diagnosis using stochastic pooling

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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Classification of coronary artery calcifications according to motion artifacts in chest CT using a convolutional neural network

10.1117/12.2253669 ◽

2017 ◽

Cited By ~ 2

Author(s):

Jurica Šprem ◽

Bob D. de Vos ◽

Pim A. de Jong ◽

Max A. Viergever ◽

Ivana Išgum

Keyword(s):

Neural Network ◽

Coronary Artery ◽

Convolutional Neural Network ◽

Chest Ct ◽

Motion Artifacts

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Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling

Journal of Medical Systems ◽

10.1007/s10916-017-0845-x ◽

2017 ◽

Vol 42 (1) ◽

Cited By ~ 57

Author(s):

Shui-Hua Wang ◽

Yi-Ding Lv ◽

Yuxiu Sui ◽

Shuai Liu ◽

Su-Jing Wang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Stochastic Pooling

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An eight-layer convolutional neural network with stochastic pooling, batch normalization and dropout for fingerspelling recognition of Chinese sign language

Multimedia Tools and Applications ◽

10.1007/s11042-019-08345-y ◽

2019 ◽

Vol 79 (21-22) ◽

pp. 15697-15715 ◽

Cited By ~ 5

Author(s):

Xianwei Jiang ◽

Mingzhou Lu ◽

Shui-Hua Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sign Language ◽

Batch Normalization ◽

Stochastic Pooling

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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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Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

STEMedicine ◽

10.37175/stemedicine.v2i8.101 ◽

2021 ◽

Vol 2 (8) ◽

pp. e101

Author(s):

Jian Wang ◽

Dimas Lima

Keyword(s):

Neural Network ◽

Multiple Sclerosis ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Early Stage ◽

Brain Mri ◽

Brain Magnetic Resonance Imaging ◽

White Matter Lesions ◽

Learning Methods ◽

Stochastic Pooling

Multiple sclerosis is one of most widespread autoimmune neuroinflammatory diseases which mainly damages body function such as movement, sensation, and vision. Despite of conventional clinical presentation, brain magnetic resonance imaging of white matter lesions is often applied to diagnose multiple sclerosis at the early stage. In this article, we proposed a 6-layer stochastic pooling convolutional neural network with multiple-way data augmentation for multiple sclerosis detection in brain MRI images. Our approach does not demand hand-crafted features unlike those traditional machine learning methods. Via application of stochastic pooling and multiple-way data augmentation, our 6-layer CNN achieved equivalent performance against those deep learning methods which consist of so many layers and parameters that ordinarily bring difficulty to training. The results showed that this 6-layer CNN obtained a sensitivity of 95.98±0.46%, a specificity of 95.67±0.92%, and an accuracy of 95.82±0.58%. According to comparison experiments, our results are better than state-of-the-art approaches. Further, we also conducted ablation experiments to examine the contribution of stochastic pooling and multiple-way data augmentation to the original CNN model. The contrast experiments revealed that our scheme of stochastic pooling and multiple-way data augmentation enhanced the original 6-layer CNN model compared to those using maximum pooling or average pooling and inadequate data augmentation.

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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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