The Impact of AI in COVID-19: AI-Powered Diagnostics, Epidemiology

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

International Journal of Engineering Applied Sciences and Technology

The prominence of COVID-19 has already been attributed to a current Forward Transcribed – Polymerase Chain Reaction (RT-PCR) framework. behind because of confined accessibility of check units and generally low sure symptoms within the starting phases of the infection, encouraging the requirement for optionally available preparations. To develop COVID-19 ailment prediction tool based totally on Artificial Intelligence may be helped. In the proposed machine CT pictures of X-rays are skilled the use of Convolutional Neural Network strategies, which could assist device to mechanically expect the COVID-19 detection. The accuracy has established to be better than other strategies. Our proposed machine can obtain nearby 93- 94% of accuracy for detection of Covid-19 on bases of xray.

IRJET, 2021

The severe acute respiratory syndrome coronavirus (SARS CoV-2) is detected using chest X-ray images (SARS CoV-2) using chest X-ray images, principally accountable for coronavirus disease 2019 (COVID-19), is critical for both patients and doctors. Furthermore, this becomes even more important in regions where experimental kits cannot be purchased. We focused on presenting the advantage of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Thirty-eight experiments were distributed using deep neural networks, ten experiments using five machine learning models, and fourteen experiments using state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately within the experiments to gauge model performance, and eightfold cross-validation was used. We use Keras [2], which uses TensorFlow for the backend. A convolutional neural network with preprocessing and reduced layers can detect COVID-19 in a minimal number of imbalanced chest X-ray images. This project aims to boost the healthcare system in hospitals through AI.

SN Computer Science

COVID-19 also referred to as Corona Virus disease is a communicable disease that is caused by a coronavirus. Significant number of people who are tainted with this infection will have to brave and encounter moderate to severe respiratory sickness. Aged persons, sick, convalescing people and all those having underlying health complications like diabetes, chronic breathing diseases and cardiovascular diseases are bound to contract this sickness if not taken proper care of. At the current scenario, there are neither definite treatments nor inoculations against COVID-19. Nevertheless, there are numerous continuing clinical trials assessing the impending treatments and vaccines. Sensing the threatening impacts of Covid-19, researchers of computer science have started using various techniques and approaches of Machine Learning and Deep Learning to detect the presence of the disease using X-rays and CT images. The biggest stumbling block here is that there are only a few datasets available. There is also less number of experts for marking the information explicit to this new strain of infection in people. Artificial Intelligence centred tools can be designed and developed quickly for adapting the existing AI models and for leveraging the ability to modify and associating them with the preliminary clinical understanding to address the new group of COVID-19 and the novel challenges associated with it. In this paper, we look into a few techniques of Machine Learning and Deep Learning that have been employed to analyse Corona Virus Data.

IRJET, 2022

The severe acute respiratory syndrome coronavirus, which is the source of the present deadly coronavirus epidemic, was discovered in Wuhan, China. Threats to human life have been created by this pandemic's effects. Covid-19 is a fatal virus that is fast spreading and has an impact on people's daily lives, health, and economies of nations. This sickness not only harmed a single nation; it also had an impact on the entire world. Millions of individuals have been impacted by the outbreak, and the death toll has been rising alarmingly. Forecasting and careful analysis of the pattern of disease spread in such a setting might motivate the creation of better strategies and more effective decisionmaking.

International Journal of Advanced Scientific Innovation, 2021

COVID-19 outbreaks only affect the lives of people, they result in a negative impact on the economy of the country. On Jan. 30, 2020, it was declared as a health emergency for the entire globe by the World Health Organization (WHO). By Apr. 28, 2020, more than 3 million people were infected by this virus and there was no vaccine to prevent. The WHO released certain guidelines for safety, but they were only precautionary measures. The use of information technology with a focus on fields such as data Science and machine learning can help in the fight against this pandemic. It is important to have early warning methods through which one can forecast how much the disease will affect society, on the basis of which the government can take necessary actions without affecting its economy. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-smallsized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. preprocessing stages of the datasets performed in this study include dataset balancing, medical experts' image analysis, and data augmentation. experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain.

Electronics

The coronavirus epidemic (COVID-19) is growing quickly around the globe. The first acute atypical respiratory illness was reported in December 2019, in Wuhan, China. This quickly spread from Wuhan city to other locations. Deep learning (DL) algorithms are one of the greatest solutions for consistently and readily recognizing COVID-19. Previously, many researchers used state-of-the-art approaches for the classification of COVID-19. In this paper, we present a deep learning approach with the EfficientnetB4 model, centered on transfer learning, for the classification of COVID-19. Transfer learning is a popular technique that uses pre-trained models that have been trained on the ImageNet database and employed on a new problem to increase generalization. We presented an in-depth training approach to extract the visual properties of COVID-19 in exchange for providing a medical assessment before infection testing. The proposed methodology is assessed on a publicly accessible X-ray imaging ...

Current Medical Imaging Formerly Current Medical Imaging Reviews, 2021

Background: This paper provides a systematic review of the application of Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep Learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). Objective & Method: The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and Computed Tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of...

arXiv (Cornell University), 2021

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, This survey paper explores the proposed methodologies that can aid doctors and researchers in adopting early and inexpensive methods to diagnose the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and after analyzing it, future directives are suggested to build an advanced, more developed health industry.

IEEE Access, 2021

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19. Coronavirus, deep learning, deep transfer learning, diagnosis, computer tomography.