Covid-19 Recognition by Chest CT and Deep Learning

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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Efficient Medical Image Segmentation Of COVID-19 Chest CT Images Based on Deep Learning Techniques

2021 International Conference on Emerging Smart Computing and Informatics (ESCI) ◽

10.1109/esci50559.2021.9397043 ◽

2021 ◽

Author(s):

Sanika Walvekar ◽

Swati Shinde

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Ct Images ◽

Medical Image Segmentation ◽

Chest Ct ◽

Learning Techniques

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A Deep Learning Approach For Improved Segmentation Of Lesions Related To Covid-19 Chest CT Scans

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi48211.2021.9434139 ◽

2021 ◽

Author(s):

Vlad Vasilescu ◽

Ana Neacsu ◽

Emilie Chouzenoux ◽

Jean-Christophe Pesquet ◽

Corneliu Burileanu

Keyword(s):

Deep Learning ◽

Chest Ct ◽

Ct Scans ◽

Learning Approach

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Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2018.2876510 ◽

2019 ◽

Vol 38 (4) ◽

pp. 991-1004 ◽

Cited By ~ 59

Author(s):

Yutong Xie ◽

Yong Xia ◽

Jianpeng Zhang ◽

Yang Song ◽

Dagan Feng ◽

...

Keyword(s):

Deep Learning ◽

Lung Nodule ◽

Chest Ct ◽

Knowledge Based ◽

Nodule Classification

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Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

10.1101/2020.03.12.20027185 ◽

2020 ◽

Cited By ~ 52

Author(s):

Chuansheng Zheng ◽

Xianbo Deng ◽

Qiang Fu ◽

Qiang Zhou ◽

Jiapei Feng ◽

...

Keyword(s):

Deep Learning ◽

Predictive Value ◽

Learning Algorithm ◽

Chest Ct ◽

Lung Region ◽

Deep Learning Algorithm ◽

High Negative Predictive Value ◽

Probability Threshold ◽

Weakly Supervised ◽

Learning Software

AbstractAccurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 detection on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning-based software system was developed using 3D CT volumes to detect COVID-19. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious. 499 CT volumes collected from Dec. 13, 2019, to Jan. 23, 2020, were used for training and 131 CT volumes collected from Jan 24, 2020, to Feb 6, 2020, were used for testing. The deep learning algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. There was an operating point with 0.907 sensitivity and 0.911 specificity in the ROC curve. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient’s CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training. The easily-trained and highperformance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.

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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 Learning Method for Quantitative Analysis of Covid -19 using CT Chest Images

10.21203/rs.3.rs-540369/v1 ◽

2021 ◽

Author(s):

Indrajeet Kumar ◽

Jyoti Rawat

Keyword(s):

Deep Learning ◽

Diagnostic Tests ◽

Ct Images ◽

Rapid Diagnosis ◽

Chest Ct ◽

Visual Features ◽

Learning Module ◽

Volumetric Ct ◽

The Cost

Abstract The manual diagnostic tests performed in laboratories for pandemic disease such as COVID19 is time-consuming, requires skills and expertise of the performer to yield accurate results. Moreover, it is very cost ineffective as the cost of test kits is high and also requires well-equipped labs to conduct them. Thus, other means of diagnosing the patients with presence of SARS-COV2 (the virus responsible for COVID19) must be explored. A radiography method like chest CT images is one such means that can be utilized for diagnosis of COVID19. The radio-graphical changes observed in CT images of COVID19 patient helps in developing a deep learning-based method for extraction of graphical features which are then used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID19 from given volumetric CT images of patient’s chest by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network is deployed for classifying the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of which 349 images belong to COVID19 positive cases while remaining 397 belong negative cases of COVID19. The extensive experiment has been completed with the accuracy of 98.4 %, sensitivity of 98.5 %, the specificity of 98.3 %, the precision of 97.1 %, F1score of 97.8 %. The obtained result shows the outstanding performance for classification of infectious and non-infectious for COVID19 cases.

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