COVID‐19 disease severity assessment using CNN model

Irmak, Emrah

doi:10.1049/ipr2.12153

Cited by 62 publications

(47 citation statements)

References 28 publications

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“…The CXR severity score was adapted from Irmak [9] and Wong et al [26], and it was between 0 and 14 by summing up the opacity (0-6) and involvement (0-8). The total severity score summed the individual scores of both lungs.…”

Section: Chest X-ray Image Acquisition and Radiologist Reportmentioning

confidence: 99%

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COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis

Ghenea

Udriştoiu

Neaga

et al. 2021

Life

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(1) Background: The new SARS-COV-2 pandemic overwhelmed intensive care units, clinicians, and radiologists, so the development of methods to forecast the diagnosis’ severity became a necessity and a helpful tool. (2) Methods: In this paper, we proposed an artificial intelligence-based multimodal approach to forecast the future diagnosis’ severity of patients with laboratory-confirmed cases of SARS-CoV-2 infection. At hospital admission, we collected 46 clinical and biological variables with chest X-ray scans from 475 COVID-19 positively tested patients. An ensemble of machine learning algorithms (AI-Score) was developed to predict the future severity score as mild, moderate, and severe for COVID-19-infected patients. Additionally, a deep learning module (CXR-Score) was developed to automatically classify the chest X-ray images and integrate them into AI-Score. (3) Results: The AI-Score predicted the COVID-19 diagnosis’ severity on the testing/control dataset (95 patients) with an average accuracy of 98.59%, average specificity of 98.97%, and average sensitivity of 97.93%. The CXR-Score module graded the severity of chest X-ray images with an average accuracy of 99.08% on the testing/control dataset (95 chest X-ray images). (4) Conclusions: Our study demonstrated that the deep learning methods based on the integration of clinical and biological data with chest X-ray images accurately predicted the COVID-19 severity score of positive-tested patients.

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Section: Chest X-ray Image Acquisition and Radiologist Reportmentioning

confidence: 99%

“…In [9,10], chest X-ray images were investigated to differentiate lung changes produced by COVID-19 disease. These studies demonstrated that the prediction of COVID-19 disease severity could be also established based on lung changes as ground-glass opacity, lungs' involvement, consolidation, bilateral infiltration, and vascular enlargement.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis

Ghenea

Udriştoiu

Neaga

et al. 2021

Life

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“…COVID-19 recurrence is associated with subpleural exudation towards the lung periphery and severe respiratory failure at discharge. (Irmak 2021) provides a unique COVID-19 illness severity classification approach based on a convolutional neural network (CNN). Using chest X-ray images as input, an automated CNN model is constructed and proposed to split COVID-19 patients into four severity classes: mild, moderate, severe, and critical with an average accuracy of 95.52 percent.…”

Section: (Mckee Et Al 2015)mentioning

confidence: 99%

“…The metric for measuring performance the ratio of True Positives (TP) to the sum of False Negatives (FN) and True Positives derived from a dataset of clinical specimens is used to recall determine in (9).…”

Section: Recallmentioning

confidence: 99%

Detection and Classification of COVID-19 Using Machine Learning

Shahin

Alshammari

Taloba

et al. 2021

Preprint

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As people all over the world are vulnerable to be affected by the COVID-19 virus, the automatic detection of such a virus is an important concern. The paper aims to detect and classify coronavirus using machine learning. To spot and identify coronavirus in CT-Lung screening and Computer-Aided diagnosis (CAD) system is projected to distinguish and classifies the COVID-19. By utilizing the clinical specimens obtained from the corona-infected patients with the help of some machine learning techniques like Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function. While some specialists believe that the RT-PCR test is the best option for diagnosing Covid-19 patients, others believe that CT scans of the lungs can be more accurate in diagnosing coronavirus infection, as well as being less expensive than the PCR test. The clinical specimens include serum specimens, respiratory secretions, and whole blood specimens. Overall, 15 factors are measured from these specimens as the result of the previous clinical examinations. The proposed CAD system consists of four phases starting with the CT lungs screening collection, followed by a pre-processing stage to enhance the appearance of the ground glass opacities (GGOs) nodules as they originally lock hazy with fainting contrast. A modified K-means algorithm will be used to detect and segment these regions. Finally, we will use the detected, infected areas that obtained in the detection phase with a scale of 50x50 and we will crop the solid false positives that seem to be GGOs as inputs and targets for the machine learning classifiers, here we will use a support vector machine (SVM) and Radial basis function (RBF). Moreover, a GUI application is developed which avoids the confusion of the doctors for getting the exact results by giving the 15 input factors obtained from the clinical specimens.

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“…Likewise, in Ref. [ 16 ] researchers proposed a new CNN‐based approach for the classification of COVID‐19 severity. This CNN divides and categorizes COVID‐19 patients into one of four severity groups: mild, moderate, severe, and critical.…”

Section: Introductionmentioning

confidence: 99%

Randomly initialized convolutional neural network for the recognition of COVID‐19 using X‐ray images

Atitallah

Driss

Boulila

et al. 2021

Int J Imaging Syst Tech

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By the start of 2020, the novel coronavirus (COVID‐19) had been declared a worldwide pandemic, and because of its infectiousness and severity, several strands of research have focused on combatting its ongoing spread. One potential solution to detecting COVID‐19 rapidly and effectively is by analyzing chest X‐ray images using Deep Learning (DL) models. Convolutional Neural Networks (CNNs) have been presented as particularly efficient techniques for early diagnosis, but most still include limitations. In this study, we propose a novel randomly initialized CNN (RND‐CNN) architecture for the recognition of COVID‐19. This network consists of a set of differently‐sized hidden layers all created from scratch. The performance of this RND‐CNN is evaluated using two public datasets: the COVIDx and the enhanced COVID‐19 datasets. Each of these datasets consists of medical images (X‐rays) in one of three different classes: chests with COVID‐19, with pneumonia, or in a normal state. The proposed RND‐CNN model yields encouraging results for its accuracy in detecting COVID‐19 results, achieving 94% accuracy for the COVIDx dataset and 99% accuracy on the enhanced COVID‐19 dataset.

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COVID‐19 disease severity assessment using CNN model

Cited by 62 publications

References 28 publications

COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis

COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis

Detection and Classification of COVID-19 Using Machine Learning

Randomly initialized convolutional neural network for the recognition of COVID‐19 using X‐ray images

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