Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

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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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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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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COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

10.2196/preprints.19407 ◽

2020 ◽

Author(s):

Jinseok Lee

Keyword(s):

Deep Learning ◽

Diagnostic Performance ◽

Ct Images ◽

Chest Ct ◽

University Hospital ◽

Detection Accuracy ◽

Ct Image ◽

Test Dataset ◽

Learning Framework ◽

Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning

Frontiers in Oncology ◽

10.3389/fonc.2021.700210 ◽

2021 ◽

Vol 11 ◽

Author(s):

He Sui ◽

Ruhang Ma ◽

Lin Liu ◽

Yaozong Gao ◽

Wenhai Zhang ◽

...

Keyword(s):

Esophageal Cancer ◽

Deep Learning ◽

Cancer Detection ◽

Ct Images ◽

Chest Ct ◽

Validation Dataset ◽

Esophageal Wall ◽

Detection Model ◽

Model Training ◽

Sensitivity Specificity

ObjectiveTo develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images.MethodsWe retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer. The CNN is a VB-Net segmentation network that segments the esophagus and automatically quantifies the thickness of the esophageal wall and detect positions of esophageal lesions. To validate this model, 52 false negatives and 48 normal cases were collected further as the second dataset. The average performance of three radiologists and that of the same radiologists aided by the model were compared.ResultsThe sensitivity and specificity of the esophageal cancer detection model were 88.8% and 90.9%, respectively, for the validation dataset set. Of the 52 missed esophageal cancer cases and the 48 normal cases, the sensitivity, specificity, and accuracy of the deep learning esophageal cancer detection model were 69%, 61%, and 65%, respectively. The independent results of the radiologists had a sensitivity of 25%, 31%, and 27%; specificity of 78%, 75%, and 75%; and accuracy of 53%, 54%, and 53%. With the aid of the model, the results of the radiologists were improved to a sensitivity of 77%, 81%, and 75%; specificity of 75%, 74%, and 74%; and accuracy of 76%, 77%, and 75%, respectively.ConclusionsDeep learning-based model can effectively detect esophageal cancer in unenhanced chest CT scans to improve the incidental detection of esophageal cancer.

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

Walailak Journal of Science and Technology (WJST) ◽

10.48048/wjst.2021.13109 ◽

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

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

Clinical Radiology ◽

10.1016/j.crad.2020.03.002 ◽

2020 ◽

Vol 75 (5) ◽

pp. 335-340 ◽

Cited By ~ 24

Author(s):

X. Zhao ◽

B. Liu ◽

Y. Yu ◽

X. Wang ◽

Y. Du ◽

...

Keyword(s):

Ct Images ◽

Chest Ct ◽

Clinical Value ◽

Novel Coronavirus

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Evaluation of the Usefulness of CO-RADS for Chest CT in Patients Suspected of Having COVID-19

Diagnostics ◽

10.3390/diagnostics10090608 ◽

2020 ◽

Vol 10 (9) ◽

pp. 608 ◽

Cited By ~ 2

Author(s):

Tomoyuki Fujioka ◽

Marie Takahashi ◽

Mio Mori ◽

Junichi Tsuchiya ◽

Emi Yamaga ◽

...

Keyword(s):

Interobserver Agreement ◽

Characteristic Curve ◽

Intraclass Correlation ◽

Ct Images ◽

Chest Ct ◽

Rt Pcr ◽

Radiological Society ◽

Significant Difference ◽

The Individual ◽

Sensitivity Specificity

The purpose of this study was to use the Coronavirus Disease 2019 (COVID-19) Reporting and Data System (CO-RADS) to evaluate the chest computed tomography (CT) images of patients suspected of having COVID-19, and to investigate its diagnostic performance and interobserver agreement. The Dutch Radiological Society developed CO-RADS as a diagnostic indicator for assessing suspicion of lung involvement of COVID-19 on a scale of 1 (very low) to 5 (very high). We investigated retrospectively 154 adult patients with clinically suspected COVID-19, between April and June 2020, who underwent chest CT and reverse transcription-polymerase chain reaction (RT-PCR). The patients’ average age was 61.3 years (range, 21–93), 101 were male, and 76 were RT-PCR positive. Using CO-RADS, four radiologists evaluated the chest CT images. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Interobserver agreement was calculated using the intraclass correlation coefficient (ICC) by comparing the individual reader’s score to the median of the remaining three radiologists. The average sensitivity was 87.8% (range, 80.2–93.4%), specificity was 66.4% (range, 51.3–84.5%), and AUC was 0.859 (range, 0.847–0.881); there was no significant difference between the readers (p > 0.200). In 325 (52.8%) of 616 observations, there was absolute agreement among observers. The average ICC of readers was 0.840 (range, 0.800–0.874; p < 0.001). CO-RADS is a categorical taxonomic evaluation scheme for COVID-19 pneumonia, using chest CT images, that provides outstanding performance and from substantial to almost perfect interobserver agreement for predicting COVID-19.

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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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A Dual-Stage Vocabulary of Features (VoF)-Based Technique for COVID-19 Variants’ Classification

Applied Sciences ◽

10.3390/app112411902 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11902

Author(s):

Sonain Jamil ◽

MuhibUr Rahman

Keyword(s):

Feature Vector ◽

Classification Error ◽

X Ray ◽

Learning Framework ◽

Spatial Features ◽

Proposed Model ◽

Chest X Ray ◽

Dual Stage ◽

Novel Coronavirus ◽

Sensitivity Specificity

Novel coronavirus, known as COVID-19, is a very dangerous virus. Initially detected in China, it has since spread all over the world causing many deaths. There are several variants of COVID-19, which have been categorized into two major groups. These groups are variants of concern and variants of interest. Variants of concern are more dangerous, and there is a need to develop a system that can detect and classify COVID-19 and its variants without touching an infected person. In this paper, we propose a dual-stage-based deep learning framework to detect and classify COVID-19 and its variants. CT scans and chest X-ray images are used. Initially, the detection is done through a convolutional neural network, and then spatial features are extracted with deep convolutional models, while handcrafted features are extracted from several handcrafted descriptors. Both spatial and handcrafted features are combined to make a feature vector. This feature vector is called the vocabulary of features (VoF), as it contains spatial and handcrafted features. This feature vector is fed as an input to the classifier to classify different variants. The proposed model is evaluated based on accuracy, F1-score, specificity, sensitivity, specificity, Cohen’s kappa, and classification error. The experimental results show that the proposed method outperforms all the existing state-of-the-art methods.

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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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