A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

John, Christopher Clement; Ponnusamy, Vijayakumar; Chandrasekaran, Sriharipriya Krishnan; Nandakumar, Ratna

doi:10.1109/rbme.2021.3069213

Cited by 51 publications

(26 citation statements)

References 62 publications

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“…In the field of diagnosis, many studies used Artificial Intelligence techniques to process X-ray images and detect the effect in terms of percentage of the virus in a patient's lungs. Deep learning approaches are the most frequently used in image classification to achieve better results than those using traditional ML approaches [33]. In this section, we limit our investigation to some research using TL technique-based models to diagnose COVID-19 [34].…”

Section: Review Of Some Related Workmentioning

confidence: 99%

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images

Hamida

Gannour

Cherradi

et al. 2021

Journal of Healthcare Engineering

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COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies.

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Section: Review Of Some Related Workmentioning

confidence: 99%

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images

Hamida

Gannour

Cherradi

et al. 2021

Journal of Healthcare Engineering

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“…Regarding machine learning applications, Chamola et al [16] provided a review of several machine learning algorithms that can be adopted in disaster and pandemic management, also presenting a tutorial on machine learning algorithms. Mathematical models, including compartment, statistical and machine learning models for COVID-19 transmission and diagnosis were also reviewed in a work presented by John et al [17].…”

Section: Background and Related Workmentioning

confidence: 99%

Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases—A Systematic Literature Review

Leite

Albuquerque

Pinheiro

2021

IJERPH

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With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.

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“…The COVID-19 pandemic has given rise to an extraordinary degree of worldwide scientific cooperation. Machine learning and deep learning based algorithms are very helpful in the fight against COVID-19 in many aspects [ 5 ]. These algorithms also allow the research community and clinicians a vast quantity of data evaluation for COVID-19 distribution forecasting.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

Habib

Alsanea²,

Aloraini

et al. 2022

Sensors

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Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods. Early face mask detection is very important to prevent the spread of COVID-19. For this purpose, we investigate several deep learning-based architectures such as VGG16, VGG19, InceptionV3, ResNet-101, ResNet-50, EfficientNet, MobileNetV1, and MobileNetV2. After these experiments, we propose an efficient and effective model for face mask detection with the potential to be deployable over edge devices. Our proposed model is based on MobileNetV2 architecture that extracts salient features from the input data that are then passed to an autoencoder to form more abstract representations prior to the classification layer. The proposed model also adopts extensive data augmentation techniques (e.g., rotation, flip, Gaussian blur, sharping, emboss, skew, and shear) to increase the number of samples for effective training. The performance of our proposed model is evaluated on three publicly available datasets and achieved the highest performance as compared to other state-of-the-art models.

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A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

Cited by 51 publications

References 62 publications

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images

Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases—A Systematic Literature Review

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

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