Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans

Tabrizchi, Hamed; Mosavi, Amir; Szabo-Gali, Akos; Nádai, László

doi:10.20944/preprints202010.0290.v1

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Furthermore, in [8], Anastassopouloua et al used the Susceptible-Infected-Recovered-Dead (SIRD) model to calculate the basic reproduction number, infection, and per day recovery rate for the data generated from China. Recent approaches in dealing with infectious diseases such as COVID-19 using data-driven methods such as machine learning and deep learning, hybrid artificial intelligence, and principal component analysis can be found in [12][13][14][15][16][17]. However, COVID-19 is rare, complex, and many things are yet unknown, which set limitations to what known models (especially the integer-order models) could capture.…”

Section: Introductionmentioning

confidence: 99%

System of Time Fractional Models for COVID-19: Modeling, Analysis and Solutions

et al. 2021

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The emergence of the COVID-19 outbreak has caused a pandemic situation in over 210 countries. Controlling the spread of this disease has proven difficult despite several resources employed. Millions of hospitalizations and deaths have been observed, with thousands of cases occurring daily with many measures in place. Due to the complex nature of COVID-19, we proposed a system of time-fractional equations to better understand the transmission of the disease. Non-locality in the model has made fractional differential equations appropriate for modeling. Solving these types of models is computationally demanding. Our proposed generalized compartmental COVID-19 model incorporates effective contact rate, transition rate, quarantine rate, disease-induced death rate, natural death rate, natural recovery rate, and recovery rate of quarantine infected for a holistic study of the coronavirus disease. A detailed analysis of the proposed model is carried out, including the existence and uniqueness of solutions, local and global stability analysis of the disease-free equilibrium (symmetry), and sensitivity analysis. Furthermore, numerical solutions of the proposed model are obtained with the generalized Adam–Bashforth–Moulton method developed for the fractional-order model. Our analysis and solutions profile show that each of these incorporated parameters is very important in controlling the spread of COVID-19. Based on the results with different fractional-order, we observe that there seems to be a third or even fourth wave of the spike in cases of COVID-19, which is currently occurring in many countries.

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Section: Introductionmentioning

confidence: 99%

System of Time Fractional Models for COVID-19: Modeling, Analysis and Solutions

et al. 2021

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“…Recently, researchers have developed studies for the prediction of COVID-19 cases and death rate using ML-based methods. Tabrizchi et al (2020) employed a Deep Learning-based estimation model integrated by an image-based diagnosis method to detect coronavirus infection [9]. Dutta et al (2020) used a DL based prediction model for the estimation of the COVID-19 outbreak [10].…”

Section: Introductionmentioning

confidence: 99%

Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer

Ardabili

Mosavi

Band

et al. 2020

Preprint

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An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences of infectious diseases. Recently, machine learning (ML) based prediction models have been successfully employed for the prediction of the disease outbreak. The present study aimed to engage an artificial neural network-integrated by grey wolf optimizer for COVID-19 outbreak predictions by employing the Global dataset. Training and testing processes have been performed by time-series data related to January 22 to September 15, 2020 and validation has been performed by time-series data related to September 16 to October 15, 2020. Results have been evaluated by employing mean absolute percentage error (MAPE) and correlation coefficient (r) values. ANN-GWO provided a MAPE of 6.23, 13.15 and 11.4% for training, testing and validating phases, respectively. According to the results, the developed model could successfully cope with the prediction task.

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“…Many individuals who got affected have associations with the live animal's food market. This made the analysts believe that the spread happens because of association with live food market [2]. On additional examination, Coronavirus cases were coming up which had no association with live food market, like voyaging and exportation of merchandise.…”

mentioning

confidence: 99%

Detection and Severity Identification of Covid-19 in Chest X-ray Images Using Deep Learning

Narasimha¹,

Dhanalakshmi

2022

IJEER

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COVID-19 pandemic is causing a significant flare-up, seriously affecting the wellbeing and life of many individuals all around the world. One of the significant stages in battling COVID-19 is the capacity to recognize the tainted patients early and put them under exceptional consideration. In the proposed model we used deep learning-based exception Net under transfer learning paradigm. We trained the proposed model using chest-X rays collected from the open-source dataset (COVID -19 Dataset) using K10 cross-validation. We further calculated the severity in the covid classified images by the model using radiologist ground truth. We achieved an accuracy of 96.1% in the classification, and we are able to calculate the severity of the COVID -19 within the range of 75-100 % risk. Our proposed model successfully classified the COVID chest x-rays with severity measure.

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Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans

Cited by 3 publications

References 2 publications

System of Time Fractional Models for COVID-19: Modeling, Analysis and Solutions

System of Time Fractional Models for COVID-19: Modeling, Analysis and Solutions

Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer

Detection and Severity Identification of Covid-19 in Chest X-ray Images Using Deep Learning

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