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

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

doi:10.1101/2020.12.20.20248582

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…There are many types of traditional classification methods, such as SVM, K-NN, decision tree, random forest, AdaBoost, XGBoost, and Bagging. Tabrizchi et al [50] utilized EL to improve the classification accuracy of COVID-19. They used SVM, artificial neural network (ANN), Naive Bayes, and CNN for classification.…”

Section: Classificationmentioning

confidence: 99%

A Survey on Machine Learning in COVID-19 Diagnosis

Guo¹,

Zhang²,

Lu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What's more, the advantages and limitations of different diagnosis methods are compared. Although the great achievements in medical images classification in recent years, Corona Virus Disease 2019 images classification based on machine learning still encountered many problems. For example, the highly unbalanced dataset, the difficulty of collecting labeled data, and the poor quality of the data. Aiming at these problems, we propose some solutions and provide a comprehensive presentation for future research.

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Section: Classificationmentioning

confidence: 99%

A Survey on Machine Learning in COVID-19 Diagnosis

Guo¹,

Zhang²,

Lu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

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“…Table 1 includes the fundamental models of the pandemic [1,2,12]. Further advanced SIRDbased models and data-driven had been investigated in [13][14][15][16][17][18][19][20][21]. The above results show that social distancing, quarantine, and isolation of infected populations support in controlling of an epidemic.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear control of Covid-19 pandemic based on the SIRD model

Ghadami

Seyyedi

Rituraj

2022

2022 IEEE 20th Jubilee International Symposium on Intelligent Systems and Informatics (SISY)

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In the last month of 2019, a new version of Corona disease was observed in Wuhan (China) which is known as Covid-19. Several models have been proposed to predict disease treatment. The SIR model is considered one of the simplest models for the prediction of pandemic disease. This means susceptible (S), infected (I), and recovered (R) populations. The SIRD model is yet another method that includes one more equation, i.e., the number of deaths (D). This paper proposed a control law for the first time to prevent the progression of the disease. The proposed control law is based on the SIRD model that is determined using two methods, i.e., the input-state feedback linearization method and the input-output feedback linearization method for the nonlinear modeling of Covid-19. The goal of control in this model is to reduce the percentage or number of infected people and the number of deaths due to Covid-19 disease. Simulation results show that the feedback linearization methods can have positive results in a significant reduction in unfurl of Covid-19. Delay in quarantine of infected people and constant percentage of people who should be quarantined are investigated as two important parameters. Results show that the percentage of infected people decreases 96.3 % and the percentage of deaths decreases 93.6 % when delay in quarantine equals 7 weeks.

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AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

Karthik

Menaka

Hariharan

et al. 2022

IRBM

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Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.

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

Cited by 4 publications

References 23 publications

A Survey on Machine Learning in COVID-19 Diagnosis

A Survey on Machine Learning in COVID-19 Diagnosis

Nonlinear control of Covid-19 pandemic based on the SIRD model

AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

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