A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

Xu, Xiaowei; Jiang, Xieyuan; Ma, Chen; Du, Peng; Li, Xukun; Lv, Shihui; Yu, Liang; Ni, Qin; Chen, Yanfei; Su, Junwei; Lang, Guanjing; Li, Yongtao; Zhao, Hong; Li, Jun; Xu, Kaijin; Ruan, Lifang; Sheng, Jifang; Qiu, Yunqing; Wu, Wei; Liang, Tingbo; Li, Lanjuan

doi:10.1016/j.eng.2020.04.010

Cited by 1,089 publications

(882 citation statements)

References 34 publications

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“…The proposed model is able to detect suspected COVID-19 thoracic CT features with high accuracy. CT image analysis is also implemented in [82] to early screen COVID-19 patients. The finding results indicate that the manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia.…”

Section: B Coronavirus Detectionmentioning

confidence: 99%

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Nguyen¹,

Ding²,

Pathirana³

et al. 2020

Preprint

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The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

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Section: B Coronavirus Detectionmentioning

confidence: 99%

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Nguyen¹,

Ding²,

Pathirana³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…[7] has used a concatenated Xception and ResNet50 v2. Another work [8] has used ResNet to detect viral pneumonia and Covid-19 patients. Unavailability of large number of image data of Covid-19 +ve patients is a challenge faced by most researchers working in this area.…”

Section: Related Workmentioning

confidence: 99%

Automatic COVID-19 Detection from X-Ray images using Ensemble Learning with Convolutional Neural Network

Das

Ghosh

Thunder

et al. 2020

Preprint

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Covid-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest X-Ray being the most easily available and least expensive option. In this paper we have proposed a Deep Convolutional Neural Network based solution which can detect the Covid-19 +ve patients using chest X-Ray images. To test the efficacy of the solution we have used publicly available chest X-ray images of Covid +ve and -ve cases. 538 images of Covid +ve patients and 468 images of Covid -ve patients have been divided into 771 trainable images and 235 testing images. Our solution gave a classification accuracy of 95.7% and sensitivity of 98% in the test set-up. We have developed a GUI application for public use. This application can be used on any computer by any medical personnel to detect Covid +ve patients using Chest X-Ray images within a very few seconds.

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“…It is known that convolutional neural networks (CNNs) are quite powerful in data mining, image classi cation/detection, and computer vision. Many research groups have applied deep learning methods into COVID-19 computer aided diagnosis [13,[15][16][17]. But to our best knowledge, no studies were focused on the identi cation of severity of infected patients, although this identi cation is a crucial evaluation criterion to develop proper therapeutic treatment strategy.…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features

Sun

et al. 2020

Preprint

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Background: To implement the real-time diagnosis of the severity of patients infected with novel coronavirus 2019 (COVID-19) and guide the follow-up therapeutic treatment, We collected chest CT scans of 202 patients diagnosed with the COVID-19 from three hospitals in Anhui Province, China.Methods: A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. Four pre-trained deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) with multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) were applied to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, 10-fold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines. Results and conclusion: The experimental results demonstrate that classification of the features from pre-trained deep models show the promising application in COVID-19 screening whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for 10-fold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.

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A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia

Cited by 1,089 publications

References 34 publications

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Automatic COVID-19 Detection from X-Ray images using Ensemble Learning with Convolutional Neural Network

Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features

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