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Past couple of years, the world is going through one of the biggest pandemic named COVID-19. In the mid of year 2019, it is a very difficult process to predict the COVID-19 just by viewing the images. Later on AI based technology has done a significant role in the prediction of COVID-19 through biomedical images such as CT scan, X ray etc. This stu...
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... Darji et al. demonstrated a prediction model for COVID-19 based on chest X-ray images. This model consisted of two sub-models that called the U-Net and the capsule network [25]. Li et al. introduced a model to select the "k" value and determined the penalty factor "α" values. ...
In recent decades, many infectious diseases have appeared that have negatively affected life in general and people in particular, causing many economic and human losses. Recently, many attempts have emerged to confront these diseases using computer-based technology for diagnosis, prediction, and data analysis using various techniques, the most important of which is deep learning. Previous research relied primarily on a set of images taken from the patient’s body while he was in a healthcare facility, and this is the main weakness of these studies. Not all people go to a doctor or hospital when they feel the symptoms of a disease. Hence, people moving in crowded places without knowing their health status can contribute to spreading infectious diseases quickly, and this is the issue that should be confronted. Therefore, this paper presents a people-monitoring scheme, which is based on the internet of things (IoT) technology, to predict infectious disease symptoms through people’s behavior as well as through a wireless body area network (WBAN). This scheme can predict the spread of disease by tracking the movements of infected persons. Additionally, a simple methodology for processing the data extracted from the monitoring process across a range of different computing centers is introduced. Moreover, to ensure the monitoring scheme operates in real-time, it was necessary to provide a powerful coverage model for its objects. Also, a simple COVID-19 case study is presented. Finally, the performance of the prediction model is measured using images, sounds and videos files. Furthermore, the performance of the data computing and coverage methodologies is measured using an intensive simulation environment for the IoT that was constructed using NS3 package. The results showed that the proposed scheme is able to predict the symptoms of disease and its spread with accepted level of accuracy. In addition, using a mixture of coverage tools and computing techniques is recommended.
... The dataset however was smaller with 50 normal and 50 COVID-19 patients. This study [57] developed a deep learning model called XR-CAPS that incorporates a UNet model for image segmentation and a capsule network for feature extraction for the prediction of COVID-19 from CXR images. The dataset consisted of 896 patients who were either healthy, had pneumonia or were COVID-19 positive. ...
Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.
This study primarily focuses on a novel approach to Covid-19 prediction utilizing X-ray images. The images are used for the initial stage of training of the CNN Convolution neural network model. For improved classification and prediction accuracy, images are trained and tested using a hybrid GANs (Generative Adversarial Networks based Convolution neural network) - CNN (Convulational Neural Network) model. The noise cancellation technique of image processing has been used to minimize the noise in images and used for the GANs-CNN hybrid model. Each method of the proposed model has resulted in better accuracy, in which the validation accuracy on every 15 epochs is 79.2%, 85.8%, and 87.1% respectively.
The COVID-19 pandemic has had a significant global impact, affecting public health, economies, and social structures. Accurate forecasting of the spread and severity of the disease has become crucial for effective decision-making and resource allocation. Machine learning techniques have emerged as powerful tools for COVID-19 forecasting due to their ability to analyze complex data patterns and make predictions. In this review paper, we provide an overview of the stateof-the-art machine learning approaches employed for COVID-19 forecasting, highlighting their strengths, limitations, and future directions. We discuss the different data sources used, feature engineering techniques, modelling strategies, and evaluation metrics employed in COVID-19 forecasting research. Additionally, we examine the challenges associated with COVID-19 forecasting, including data quality issues, model interpretability, and ethical considerations. We conclude by outlining potential areas for future research and emphasizing the importance of collaboration and data sharing to improve the accuracy and reliability of COVID-19 forecasting models.
Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60–70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.
