Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review

Kaur, Jaspreet; Kaur, Prabhpreet

doi:10.1007/s11831-021-09667-7

Cited by 8 publications

(4 citation statements)

References 152 publications

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“…These studies successfully adapted pre-trained models from non-medical domains to medical datasets, demonstrating the effectiveness of this approach in rapidly deploying AI solutions for emerging health crises like COVID-19. Further extending the capabilities of CNNs, studies [25] and [26] focused on grading the severity of lung infections. This approach went beyond mere detection and provided critical insights into the extent of lung involvement, which is crucial for treatment planning in COVID-19 cases.…”

Section: Related Workmentioning

confidence: 99%

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

Dzhaynakbaev,

Kurmanbekkyzy,

Baimakhanova

et al. 2024

IJACSA

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In the wake of the COVID-19 pandemic, the use of medical imaging, particularly X-ray radiography, has become integral to the rapid and accurate diagnosis of pneumonia induced by the virus. This research paper introduces a novel twodimensional Convolutional Neural Network (2D-CNN) architecture specifically tailored for the classification of COVID-19 related pneumonia in X-ray images. Leveraging the advancements in deep learning, our model is designed to distinguish between viral pneumonia, typical of COVID-19, and other types of pneumonia, as well as healthy lung imagery. The architecture of the proposed 2D-CNN is characterized by its depth and a unique layer arrangement, which optimizes feature extraction from X-ray images, thus enhancing the model's diagnostic precision. We trained our model using a substantial dataset comprising thousands of annotated X-ray images, including those of patients diagnosed with COVID-19, patients with other pneumonia types, and individuals with no lung infection. This dataset enabled the model to learn a wide range of radiographic features associated with different lung conditions. Our model demonstrated exceptional performance, achieving high accuracy, sensitivity, and specificity in preliminary tests.The results indicate that our 2D-CNN model not only outperforms existing pneumonia classification models but also provides a valuable tool for healthcare professionals in the early detection and differentiation of COVID-19 related pneumonia. This capability is crucial for prompt and appropriate treatment, potentially reducing the pandemic's burden on healthcare systems. Furthermore, the model's design allows for easy integration into existing medical imaging workflows, offering a practical and efficient solution for frontline medical facilities. Our research contributes to the ongoing efforts to combat COVID-19 by enhancing diagnostic procedures through the application of artificial intelligence in medical imaging.

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Section: Related Workmentioning

confidence: 99%

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

Dzhaynakbaev,

Kurmanbekkyzy,

Baimakhanova

et al. 2024

IJACSA

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show abstract

“…There is also an ample amount of research using ML algorithms in the medical field. DL techniques have been applied in infection monitoring [64,84,85], in developing personalized advice for treatment [19,86], in diagnosing several diseases like COVID-19 [63,[87][88][89], or imaging procedures including radiology [14,63,90,91] and pathology imaging [19] or in cancer screening [91][92][93][94].…”

Section: Domainsmentioning

confidence: 99%

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

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The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

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“…Machine learning models have been developed to predict the spread and severity of the virus using known symptoms of the virus [8][9][10]. Deep learning models have also been proposed by authors in detecting the virus using medical images [11][12][13][14]. Epidemiological models have been discovered to be relatively more effective in the overall understanding of the dynamics of the virus.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Basic Reproduction Number for the Second Wave of COVID–19 Pandemic in Nigeria

Wusu¹,

Olabanjo²,

Mazzara³

2022

Preprint

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It is no news that the COVID–19 pandemic has affected many persons in different ways. As the number of reported cases rises across the globe, efforts are geared towards production and administration of effective vaccines for the disease. However, many developing countries are faced with the dilemma of how to slow the spread and flatten the curves of the disease as the available vaccines are not enough. Interestingly, the dynamics of the disease can be analysed to get useful insights to enhance the making of suitable preventive policies that will slow the spread, ultimately flatten the curves of the disease and also help in managing any future occurrence. In this work, the aim is to analyse the dynamics, and estimate the basic reproduction number of the second wave of the pandemic in Nigeria using a Susceptible-Infected-Recovered-Deceased (SIRD) compartmental–based model. The dynamics of the disease is described by a system of nonlinear ordinary differential equations. The model takes into consideration the current control policies in place - social distancing, mask usage, personal hygiene and quarantine. Available data provided by Nigeria Centre for Disease Control (NCDC), World Health Organization (WHO) and Wolfram Data Repository were used for the computations. The Quasi–Newton algorithm was implemented in fitting the proposed model to the available data and a sensitivity analysis was presented. Major parameters - effective contact rate, average recovery time, average mortality rate, and overall effectiveness of the control policies - influencing the dynamics of the disease, and the basic reproduction numbers were estimated. The turning points of the disease during the second wave were also obtained. The proposed model gave estimated values for the parameters influencing the spread of the disease. Also, the measure of the overall effectiveness of the current control policies gave insight into how effective the measures are.

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Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review

Cited by 8 publications

References 152 publications

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Estimating the Basic Reproduction Number for the Second Wave of COVID–19 Pandemic in Nigeria

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