The characteristics and clinical value of chest CT images of novel coronavirus pneumonia

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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Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

10.1101/2020.04.16.20064709 ◽

2020 ◽

Cited By ~ 5

Author(s):

A. Amyar ◽

R. Modzelewski ◽

S. Ruan

Keyword(s):

Deep Learning ◽

Ct Images ◽

Ct Imaging ◽

Chest Ct ◽

The Novel ◽

Imaging Analysis ◽

Proposed Model ◽

Fast Spreading ◽

Novel Coronavirus ◽

Deep Learning Model

ABSTRACTThe fast spreading of the novel coronavirus COVID-19 has aroused worldwide interest and concern, and caused more than one million and a half confirmed cases to date. To combat this spread, medical imaging such as computed tomography (CT) images can be used for diagnostic. An automatic detection tools is necessary for helping screening COVID-19 pneumonia using chest CT imaging. In this work, we propose a multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Our motivation is to leverage useful information contained in multiple related tasks to help improve both segmentation and classification performances. Our architecture is composed by an encoder and two decoders for reconstruction and segmentation, and a multi-layer perceptron for classification. The proposed model is evaluated and compared with other image segmentation and classification techniques using a dataset of 1044 patients including 449 patients with COVID-19, 100 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.78 for the segmentation and an area under the ROC curve higher than 93% for the classification.

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Chest CT Images for COVID-19: Radiologists and Computer-based detection

10.1101/2020.06.27.20141531 ◽

2020 ◽

Author(s):

Qingli Dou ◽

Jiangping Liu ◽

Wenwu Zhang ◽

Yanan Gu ◽

Wan-Ting Hsu ◽

...

Keyword(s):

Computed Tomography ◽

Image Analysis ◽

Ct Images ◽

Chest Ct ◽

Image Analysis System ◽

Ct Image ◽

Chest Computed Tomography ◽

Computer Based ◽

Analysis System ◽

Novel Coronavirus

ABSTRACTBackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI system, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionsWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation.

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Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

10.21203/rs.3.rs-31313/v3 ◽

2020 ◽

Author(s):

Chenglong Liu ◽

Xiaoyang Wang ◽

Chenbin Liu ◽

Qingfeng Sun ◽

Wenxian Peng

Keyword(s):

Machine Learning ◽

Characteristic Curve ◽

Medical Image Analysis ◽

Ct Images ◽

Chest Ct ◽

Support Vector ◽

Equal Weight ◽

Gray Level ◽

Novel Coronavirus ◽

Occurrence Matrix

Abstract Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

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Chest CT Images for COVID-19: Radiologists and Computer-Based Detection

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.614207 ◽

2021 ◽

Vol 8 ◽

Author(s):

Qingli Dou ◽

Jiangping Liu ◽

Wenwu Zhang ◽

Yanan Gu ◽

Wan-Ting Hsu ◽

...

Keyword(s):

Image Analysis ◽

Ct Images ◽

Chest Ct ◽

Viral Pneumonia ◽

Image Analysis System ◽

Ct Image ◽

Significant Difference ◽

Computer Based ◽

Analysis System ◽

Novel Coronavirus

BackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest CT manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI System, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation.

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Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

10.21203/rs.3.rs-31313/v2 ◽

2020 ◽

Author(s):

Chenglong Liu ◽

Xiaoyang Wang ◽

Chenbin Liu ◽

Qingfeng Sun ◽

Wenxian Peng

Keyword(s):

Machine Learning ◽

Characteristic Curve ◽

Medical Image Analysis ◽

Ct Images ◽

Chest Ct ◽

Support Vector ◽

Equal Weight ◽

Gray Level ◽

Novel Coronavirus ◽

Occurrence Matrix

Abstract Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented base on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

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A Deep Learning Interpretable Model for Novel Coronavirus Disease (COVID-19) Screening with Chest CT Images

Journal of Biomedical Science and Engineering ◽

10.4236/jbise.2020.137014 ◽

2020 ◽

Vol 13 (07) ◽

pp. 140-152

Author(s):

Eri Matsuyama

Keyword(s):

Deep Learning ◽

Ct Images ◽

Chest Ct ◽

Interpretable Model ◽

Novel Coronavirus

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CT Imaging Features of Patients Infected with 2019 Novel Coronavirus

BIO Integration ◽

10.15212/bioi-2020-0038 ◽

2021 ◽

Author(s):

Tianhong Yao ◽

Huirong Lin ◽

Jingsong Mao ◽

Shuaidong Huo ◽

Gang Liu

Keyword(s):

Ct Imaging ◽

Chest Ct ◽

Imaging Features ◽

Ct Diagnosis ◽

Clinical Value ◽

Computed Tomographic ◽

Coronavirus Infection ◽

Novel Coronavirus ◽

Reference Document ◽

Pulmonary Ct

Novel coronavirus pneumonia is an acute, infectious pneumonia caused by a novel coronavirus infection. Computed tomographic (CT) imaging is one of the main methods to screen and diagnose patients with this disease. Here, the importance and clinical value of chest CT examination in the diagnosis of COVID-19 is expounded, and the pulmonary CT findings of COVID-19 patients in different stages are briefly summarized, thus providing a reference document for the CT diagnosis of COVID-19 patients.

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Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

10.21203/rs.3.rs-31313/v1 ◽

2020 ◽

Author(s):

Chenglong Liu ◽

Xiaoyang Wang ◽

Chenbin Liu ◽

Qingfeng Sun ◽

Wenxian Peng

Keyword(s):

Machine Learning ◽

Medical Image Analysis ◽

High Sensitivity ◽

Ct Images ◽

Chest Ct ◽

Learning Framework ◽

Novel Coronavirus ◽

Sensitivity Specificity ◽

Level Performance ◽

Fold Cross Validation

Abstract Background Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods To develop and validate an integrated machine learning framework on chest CT images for differentiating COVID-19 from common pneumonia (CP). Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed common pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. Statistical textual features of COVID-19 and CP images were extracted. After feature selection, the reserved features were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers with 10-fold cross-validation. Results The classification accuracy, precision, sensitivity, specificity and F1 score of our proposed method are 91.66%, 97.91%, 85.26%, 98.15% and 91.15% respectively. The AUC of its receiver operating characteristic is 0.98. Conclusions The experimental results indicate that the EBT algorithm with statistical textural features on chest CT for differentiating COVID-19 from common pneumonia achieved high transferability, efficiency, specificity, and impressive accuracy.

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Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images

10.1101/2020.02.23.20026930 ◽

2020 ◽

Cited By ~ 45

Author(s):

Song Ying ◽

Shuangjia Zheng ◽

Liang Li ◽

Xiang Zhang ◽

Xiaodong Zhang ◽

...

Keyword(s):

Deep Learning ◽

Ground Glass Opacity ◽

Ct Images ◽

Chest Ct ◽

Accurate Diagnosis ◽

Ct Diagnosis ◽

Accurate Identification ◽

The World ◽

Novel Coronavirus ◽

Healthy Persons

BackgroundA novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images.Materials and MethodsWe collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19.ResultsThe experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019.ConclusionsThe established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

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