Transfer Learning-Based Detection of Covid-19 Using Chest CT Scan Images

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Priyanka Yadlapalli ◽

D. Bhavana ◽

Suryanarayana Gunnam

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Ct Scan ◽

Transfer Learning ◽

Confusion Matrix ◽

Chest Ct ◽

Lung Nodules ◽

Content Type ◽

Chest Ct Scan

PurposeComputed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.Design/methodology/approachRadiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.FindingsThe collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.Originality/valueThe proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

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Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking

Applied Intelligence ◽

10.1007/s10489-021-02393-4 ◽

2021 ◽

Author(s):

Ebenezer Jangam ◽

Aaron Antonio Dias Barreto ◽

Chandra Sekhara Rao Annavarapu

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Transfer Learning ◽

Learning Transfer ◽

Automatic Detection ◽

Chest Ct ◽

X Rays ◽

Chest Ct Scan

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ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images

Multimedia Tools and Applications ◽

10.1007/s11042-021-11319-8 ◽

2021 ◽

Author(s):

Rohit Kundu ◽

Pawan Kumar Singh ◽

Massimiliano Ferrara ◽

Ali Ahmadian ◽

Ram Sarkar

Keyword(s):

Ct Scan ◽

Transfer Learning ◽

Chest Ct ◽

Learning Models ◽

Chest Ct Scan

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Cancer surgery during the COVID‐19 pandemic: The experience of a comprehensive cancer center performing preoperative screening by RT‐PCR and chest CT scan

Journal of Surgical Oncology ◽

10.1002/jso.26335 ◽

2021 ◽

Vol 123 (4) ◽

pp. 815-822

Author(s):

Joanne Guerlain ◽

Fabienne Haroun ◽

Alexandra Voicu ◽

Charles Honoré ◽

Franck Griscelli ◽

...

Keyword(s):

Ct Scan ◽

Chest Ct ◽

Cancer Surgery ◽

Comprehensive Cancer Center ◽

Cancer Center ◽

Preoperative Screening ◽

Rt Pcr ◽

Chest Ct Scan

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A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis

Scientific Reports ◽

10.1038/s41598-020-80061-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Fatemeh Khatami ◽

Mohammad Saatchi ◽

Seyed Saeed Tamehri Zadeh ◽

Zahra Sadat Aghamir ◽

Alireza Namazi Shabestari ◽

...

Keyword(s):

Polymerase Chain Reaction ◽

Reverse Transcriptase ◽

Ct Scan ◽

Predictive Value ◽

Meta Analysis ◽

Chest Ct ◽

Rt Pcr ◽

Chain Reaction ◽

Polymerase Chain ◽

Chest Ct Scan

AbstractNowadays there is an ongoing acute respiratory outbreak caused by the novel highly contagious coronavirus (COVID-19). The diagnostic protocol is based on quantitative reverse-transcription polymerase chain reaction (RT-PCR) and chests CT scan, with uncertain accuracy. This meta-analysis study determines the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR. Three main databases; PubMed (MEDLINE), Scopus, and EMBASE were systematically searched for all published literature from January 1st, 2019, to the 21st May 2020 with the keywords "COVID19 virus", "2019 novel coronavirus", "Wuhan coronavirus", "2019-nCoV", "X-Ray Computed Tomography", "Polymerase Chain Reaction", "Reverse Transcriptase PCR", and "PCR Reverse Transcriptase". All relevant case-series, cross-sectional, and cohort studies were selected. Data extraction and analysis were performed using STATA v.14.0SE (College Station, TX, USA) and RevMan 5. Among 1022 articles, 60 studies were eligible for totalizing 5744 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value of chest CT scan compared to RT-PCR were 87% (95% CI 85–90%), 46% (95% CI 29–63%), 69% (95% CI 56–72%), and 89% (95% CI 82–96%), respectively. It is important to rely on the repeated RT-PCR three times to give 99% accuracy, especially in negative samples. Regarding the overall diagnostic sensitivity of 87% for chest CT, the RT-PCR testing is essential and should be repeated to escape misdiagnosis.

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The role of CT imaging for management of COVID-19 in epidemic area: early experience from a University Hospital

Insights into Imaging ◽

10.1186/s13244-020-00957-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Vikram rao Bollineni ◽

Koenraad Hans Nieboer ◽

Seema Döring ◽

Nico Buls ◽

Johan de Mey

Keyword(s):

Ct Scan ◽

Predictive Value ◽

Ct Imaging ◽

Chest Ct ◽

University Hospital ◽

Rt Pcr ◽

Ct Findings ◽

Epidemic Area ◽

Chest Ct Scan ◽

Pcr Test

Abstract Background To evaluate the clinical value of the chest CT scan compared to the reference standard real-time polymerase chain reaction (RT-PCR) in COVID-19 patients. Methods From March 29th to April 15th of 2020, a total of 240 patients with respiratory distress underwent both a low-dose chest CT scan and RT-PCR tests. The performance of chest CT in diagnosing COVID-19 was assessed with reference to the RT-PCR result. Two board-certified radiologists (mean 24 years of experience chest CT), blinded for the RT-PCR result, reviewed all scans and decided positive or negative chest CT findings by consensus. Results Out of 240 patients, 60% (144/240) had positive RT-PCR results and 89% (213/240) had a positive chest CT scans. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of chest CT in suggesting COVID-19 were 100% (95% CI: 97–100%, 144/240), 28% (95% CI: 19–38%, 27/240), 68% (95% CI: 65–70%) and 100%, respectively. The diagnostic accuracy of the chest CT suggesting COVID-19 was 71% (95% CI: 65–77%). Thirty-three patients with positive chest CT scan and negative RT-PCR test at baseline underwent repeat RT-PCR assay. In this subgroup, 21.2% (7/33) cases became RT-PCR positive. Conclusion Chest CT imaging has high sensitivity and high NPV for diagnosing COVID-19 and can be considered as an alternative primary screening tool for COVID-19 in epidemic areas. In addition, a negative RT-PCR test, but positive CT findings can still be suggestive of COVID-19 infection.

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