An Efficient COVID-19 Mortality Risk Prediction Model Using Deep Synthetic Minority Oversampling Technique and Convolution Neural Networks (original) (raw)
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Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19
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The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients’ mortality risk factors is essential for reducing this risk among infected individuals. For the timely examination of large datasets, new computing approaches must be created. Many machine learning (ML) techniques have been developed to predict the mortality risk factors and severity for COVID-19 patients. Contrary to expectations, deep learning approaches as well as ML algorithms have not been widely applied in predicting the mortality and severity from COVID-19. Furthermore, the accuracy achieved by ML algorithms is less than the anticipated values. In this work, three supervised deep learning predictive models are utilized to predict the mortality risk and severity for COVID-19 patients. The first one, which we refer to as CV-CNN, is built using a ...
Deep Learning based analysis of Covid-19 mortality risk
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Viral infectious diseases such as Covid-19 present a major threat to public health. Despite extreme research efforts, how, when and where such new outbreaks appear is still a source of substantial uncertainty. Deep learning (DL) is playing an increasingly important role in our lives. This paper presents one of the popular deep learning technique, Long Short Term Memory (LSTM) for prediction of Corona-Virus cases. The handcrafted feature extraction of traditional methods is less scalable on large data-sets, but deep learning algorithms perform extremely well on large data-sets, because of automatic feature extraction. Deep learning has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. This paper highlights the approaches where deep learning can be helpful to tackle the Covid-19 virus and similar outbreaks. This paper also discusses the structure and functioning of Covid-19. The utilization of different deep learning concepts like Convolutional Neural Networks, Transfer Learning for this pandemic is also highlighted.
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
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Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL te...
Significance of deep learning for Covid-19: state-of-the-art review
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Purpose The appearance of the 2019 novel coronavirus (Covid-19), for which there is no treatment or a vaccine, formed a sense of necessity for new drug discovery advances. The pandemic of NCOV-19 (novel coronavirus-19) has been engaged as a public health disaster of overall distress by the World Health Organization. Different pandemic models for NCOV-19 are being exploited by researchers all over the world to acquire experienced assessments and impose major control measures. Among the standard techniques for NCOV-19 global outbreak prediction, epidemiological and simple statistical techniques have attained more concern by researchers. Insufficiency and deficiency of health tests for identifying a solution became a major difficulty in controlling the spread of NCOV-19. To solve this problem, deep learning has emerged as a novel solution over a dozen of machine learning techniques. Deep learning has attained advanced performance in medical applications. Deep learning has the capacity of recognizing patterns in large complex datasets. They are identified as an appropriate method for analyzing affected patients of NCOV-19. Conversely, these techniques for disease recognition focus entirely on enhancing the accurateness of forecasts or classifications without the ambiguity measure in a decision. Knowing how much assurance present in a computerbased health analysis is necessary for gaining clinicians' expectations in the technology and progress treatment consequently. Today, NCOV-19 diseases are the main healthcare confront throughout the world. Detecting NCOV-19 in X-ray images is vital for diagnosis, treatment, and evaluation. Still, analytical ambiguity in a report is a difficult yet predictable task for radiologists. Method In this paper, an in-depth analysis has been performed on the significance of deep learning for Covid-19 and as per the standard search database, this is the first review research work ever made concentrating particularly on Deep Learning for NCOV-19. Conclusion The main aim behind this research work is to inspire the research community and to innovate novel research using deep learning. Moreover, the outcome of this detailed structured review on the impact of deep learning in covid-19 analysis will be helpful for further investigations on various modalities of diseases detection, prevention and finding novel solutions.
A Study and Analysis of COVID-19 Diagnosis and Approach of Deep Learning
The pandemic of Covid-19 (Coronavirus Disease 19) has devastated the world, affected millions of people, and disrupted the world economy. The cause of the Covid19 epidemic has been identified as a new variant known as Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV2). It motives irritation of a small air sac referred to as the alveoli. The alveoli make up most of the tissue in the lungs and fill the sac with mucus. Most human beings with Covid19 usually do no longer improve pneumonia. However, chest x-rays of seriously unwell sufferers can be a useful device for medical doctors in diagnosing Covid19-both CT and X-ray exhibit usual patterns of frosted glass (GGO) and consolidation. The introduction of deep getting to know and brand new imaging helps radiologists and medical practitioners discover these unnatural patterns and pick out Covid19-infected chest x-rays. This venture makes use of a new deep studying structure proposed to diagnose Covid19 by the use of chest Xrays. The suggested model in this work aims to predict and forecast the patients at risk and identify the primary COVID-19 risk variables
COVID-19 Prediction and Detection Using Deep Learning
2020
Currently, the detection of coronavirus disease 2019 (COVID-19) is one of the main challenges in the world, given the rapid spread of the disease. Recent statistics indicate that the number of people diagnosed with COVID-19 is increasing exponentially, with more than 1.6 million confirmed cases; the disease is spreading to many countries across the world. In this study, we analyse the incidence of COVID-19 distribution across the world. We present an artificial-intelligence technique based on a deep convolutional neural network (CNN) to detect COVID19 patients using real-world datasets. Our system examines chest X-ray images to identify such patients. Our findings indicate that such an analysis is valuable in COVID-19 diagnosis as X-rays are conveniently available quickly and at low costs. Empirical findings obtained from 1000 X-ray images of real patients confirmed that our proposed system is useful in detecting COVID-19 and achieves an F-measure range of 95–99%. Additionally, thre...
Prognosis Patients with COVID-19 using Deep Learning
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Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review
Current Medical Imaging Formerly Current Medical Imaging Reviews, 2021
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An Overview on Deep Leaning Application in Coronavirus (COVID-19):Diagnosis, Prediction and Effects
2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021
Recently, the coronavirus 2019 or CO VID-19, which originated in China, has spread to other countries' population. It is critical to evaluate an automated detection system for rapid alternative prediction and diagnosis in order to reduce the impact of COVID-19. Because of the constant increase in cases, there are fewer CO VID-19 available kits than are required for testing in hospitals. Deep learning methods are evolving to provide outstanding performance in the medical field. Deep learning inspired by brain structure is referred to as machine learning. This paper provides an overview of COVID-19's detection applications based on deep learning. Furthermore, a comprehensive review of the literature on deep leaning in COVID-19 disease has been illustrated. The proposed research study shows that in spite of presence of issues in medical database, where the transfer method can be used effectively.
International Journal of Scientific Research in Science and Technology, 2020
COVID-19 irruption has place the total world in associate unexampled troublesome state of affairs delivery life round the world to a daunting halt and claiming thousands of lives. because of COVID-19 unfold in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to five,212,172 and 334,915, it remains a true threat to the general public health system. This paper renders a response to combat the virus through Artificial Intelligence(AI) primarily based respiratory organ illness Classification by victimization organic process Deep Learning commonplace. The design of the projected model initial goes through a pre-processing of the input image is followed by a rise in information. Then the model begins a step to extract the characteristics followed by the training step. Finally, the model begins a classification and prediction method with a totally connected network fashioned of many classifiers. The model explains associate integrated bioinformatics approach during which completely different} aspects of information of knowledge taken from different data sources area unit place along to make the easy platforms for physicians and researchers. the most advantage of those AI-based platforms is to accelerate the method of identification and treatment of the COVID-19 illness. the foremost recent free publications and medical reports were investigated to decide on inputs and targets of the network that would facilitate reaching a reliable Artificial Neural Network-based tool for challenges related to COVID-19.