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Journal articles on the topic 'COVID-19 detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Mohammed, Abduh Hummadi, Hamdi Al Shammari Safooq, Abdulrahman Al Oraini Abdullah, Musaad Alabdulmunim Adel, Abdullah Alarifi Saud, and Ali Jarah Ahmed. "COVID-19 LABORATORY TESTING." International Journal of Life Sciences Research 10, no. 4 (2022): 64–72. https://doi.org/10.5281/zenodo.7406553.

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<strong>Abstract:</strong> Rapid and accurate laboratory diagnosis of active COVID-19 infection is one of the cornerstones of pandemic control. With the large number of tests available on the market, using the correct sample type and laboratory testing technique in the  clinical setting will be challenging for laypersons. In this mini-review, we will discuss the difference in diagnostic performance of different upper and lower airway samples and the role of blood and stool samples. We will discuss the performance characteristics of nucleic acid laboratory testing techniques in Acid Enhanc

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Malpe, Prof Kalpana. "COVID-19 Face Mask Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1312–15. http://dx.doi.org/10.22214/ijraset.2022.40005.

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Abstract: Face mask detection involves in detection the placement of the face then crucial whether or not it's a mask thereon or not. the problem is proximately cognate to general object notion to detect the categories of objects. Face identification flatly deals with identifying a particular cluster of entities i.e., Face. it's varied applications, like autonomous driving, education, police work, and so on. This paper presents a simplified approach to serve the above purpose using the basic Machine Learning (ML) packages such as TensorFlow, Keras, OpenCV and Scikit-Learn. The planned techniqu

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Echtioui, Amira, Wassim Zouch, Mohamed Ghorbel, Chokri Mhiri, and Habib Hamam. "Detection Methods of COVID-19." SLAS TECHNOLOGY: Translating Life Sciences Innovation 25, no. 6 (2020): 566–72. http://dx.doi.org/10.1177/2472630320962002.

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Since being first detected in China, coronavirus disease 2019 (COVID-19) has spread rapidly across the world, triggering a global pandemic with no viable cure in sight. As a result, national responses have focused on the effective minimization of the spread. Border control measures and travel restrictions have been implemented in a number of countries to limit the import and export of the virus. The detection of COVID-19 is a key task for physicians. The erroneous results of early laboratory tests and their delays led researchers to focus on different options. Information obtained from compute

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Kalaiselvi, Dr K. "Covid 19 Detection Using Deep Learning and Covid 19 Symptoms Checker." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–10. http://dx.doi.org/10.55041/ijsrem27783.

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Abstract--The goal of this project is to classify healthy individuals, COVID-19 cases, and viral pneumonia cases. Deep learning is a type of machine learning. It enables us to teach artificial intelligence to anticipate outcomes given a set of data.Artificial intelligence can be trained through supervised learning. Voice and facial recognition, disease diagnosis, defence, and security are all domains where deep learning is applied. Artificial neuralnetworks are represented by the word deep in deep learning. The human brain inspired artificial neural networks. It is made up of neurons, just lik

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Akram, Khelili Mohamed, Slatnia Sihem, and Kazar Okba. "Deep Learning Technique for Covid-19 Detection." Eurasia Proceedings of Health, Environment and Life Sciences 1 (December 16, 2021): 15–19. http://dx.doi.org/10.55549/ephels.3.

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Nowadays, the detection of coronavirus disease 2019 (COVID-19) is one of the main challenges in the world, due to the rapid spreading of this viral disease. Currently, new variant of covid-19 virus was discovered in south Africa, India, and United Kingdom (UK) due to the mutation of the virus. Owing this critical situation of the world health and with increased number of the cases with the absence of efficient a cure vaccine, early and accurate detection of COVID-19 is necessity of time to prevent and control this pandemic by timely quarantine and medical treatment. Chest x-ray is the most sui

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Arifin, Fatchul, Herjuna Artanto, Nurhasanah, and Teddy Surya Gunawan. "Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks." Journal of Southwest Jiaotong University 56, no. 2 (2021): 235–48. http://dx.doi.org/10.35741/issn.0258-2724.56.2.19.

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COVID-19 is a new disease with a very rapid and tremendous spread. The most important thing needed now is a COVID-19 early detection system that is fast, easy to use, portable, and affordable. Various studies on desktop-based detection using Convolutional Neural Networks have been successfully conducted. However, no research has yet applied mobile-based detection, which requires low computational cost. Therefore, this research aims to produce a COVID-19 early detection system based on chest X-ray images using Convolutional Neural Network models to be deployed in mobile applications. It is expe

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Vandit Gupta. "COVID-19 Detection using Deep Learning." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 421–25. http://dx.doi.org/10.46501/ijmtst061281.

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Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning and recognizing patterns from data that is unstructured or unlabelled. It is also known as deep neural learning or deep neural network. Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets h

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Deng, Yue, Siming Xing, Meixia Zhu, and Jinzhi Lei. "Impact of insufficient detection in COVID-19 outbreaks." Mathematical Biosciences and Engineering 18, no. 6 (2021): 9727–42. http://dx.doi.org/10.3934/mbe.2021476.

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<abstract><p>The COVID-19 (novel coronavirus disease 2019) pandemic has tremendously impacted global health and economics. Early detection of COVID-19 infections is important for patient treatment and for controlling the epidemic. However, many countries/regions suffer from a shortage of nucleic acid testing (NAT) due to either resource limitations or epidemic control measures. The exact number of infective cases is mostly unknown in counties/regions with insufficient NAT, which has been a major issue in predicting and controlling the epidemic. In this paper, we propose a mathemati

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Kou, Ziyi, Lanyu Shang, Yang Zhang, and Dong Wang. "HC-COVID." Proceedings of the ACM on Human-Computer Interaction 6, GROUP (2022): 1–25. http://dx.doi.org/10.1145/3492855.

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The proliferation of social media has promoted the spread of misinformation that raises many concerns in our society. This paper focuses on a critical problem of explainable COVID-19 misinformation detection that aims to accurately identify and explain misleading COVID-19 claims on social media. Motivated by the lack of COVID-19 relevant knowledge in existing solutions, we construct a novel crowdsource knowledge graph based approach to incorporate the COVID-19 knowledge facts by leveraging the collaborative efforts of expert and non-expert crowd workers. Two important challenges exist in devel

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Benny, Cijil. "AI INFLUENCE IN COVID-19 DETECTION." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 1116–20. http://dx.doi.org/10.51201/jusst/21/07235.

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This paper is on analyzing the feasibility of AI studies and the involvement of AI in COVID interrelated treatments. In all, several procedures were reviewed and studied. It was on point. The best-analyzing methods on the studies were Susceptible Infected Recovered and Susceptible Exposed Infected Removed respectively. Whereas the implementation of AI is mostly done in X-rays and CT- Scans with the help of a Convolutional Neural Network. To accomplish the paper several data sets are used. They include medical and case reports, medical strategies, and persons respectively. Approaches are being

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Reddy, M. Srilekha. "Covid-19 Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3835–540. http://dx.doi.org/10.22214/ijraset.2021.35813.

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Recently, the virus (COVID-19) has spread widely throughout the world and has led to the examination of large numbers of suspected cases using standard COVID-19 tests and has become pandemic. Everyday life, public health and the global economy have been destroyed. The pathogenic laboratory tests such as Polymerase chain reaction (PCR) take a long time with false negative results and are considered the gold standard for diagnosis. Therefore, there was an urgent need for rapid and accurate diagnostic methods to detect COVID-19 cases as soon as possible to prevent the spread of this epidemic and

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Wadia, Reena. "COVID-19 detection via oropharyngeal secretion." British Dental Journal 229, no. 4 (2020): 252. http://dx.doi.org/10.1038/s41415-020-2062-0.

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Magdy, Caroline, Fatma Issam, Pierre Amir, Sandra Adel, and Sherif H. ElGohary. "COVID-19 detection using SERS technique." MOJ Applied Bionics and Biomechanics 4, no. 4 (2020): 86–91. http://dx.doi.org/10.15406/mojabb.2020.04.00141.

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Using Surface-enhanced Raman spectroscopy (SERS) can be a more promising choice for obtaining a faster detection of COVID-19 than the PCR. PCR protocol amplifies the single segment of DNA to 100 billion copies after 40 doubling cycles to provide sufficient intensities of fluorescent signals for virus detection, which takes hours. In this paper, we proposed that the enhanced signals of SERS, which can identify single nucleotide polymorphisms and unique gene sequences, can provide a more economic and accurate detection that does not require the long time needed for DNA amplification of COVID-19.

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Jalil, Zunera, Ahmed Abbasi, Abdul Rehman Javed, et al. "A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan." Computational Intelligence and Neuroscience 2022 (August 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/6354579.

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Coronavirus (COVID-19) is a highly severe infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). The polymerase chain reaction (PCR) test is essential to confirm the COVID-19 infection, but it has certain limitations, including paucity of reagents, is computationally time-consuming, and requires expert clinicians. Clinicians suggest that the PCR test is not a reliable automated COVID-19 patient detection system. This study proposed a machine learning-based approach to evaluate the PCR role in COVID-19 detection. We collect real data containing 603 COVID-19 samples from th

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Abu Bakar, Asyraf Hakimi, Noorhashimah Mohamad Nor, Zainovia Lockman, Nor Azizah Parmin, and Khairunisak Abdul Razak. "Development of Biosensor for early Detection of COVID-19." Applied Mechanics and Materials 918 (January 9, 2024): 131–38. http://dx.doi.org/10.4028/p-2ggh9b.

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Infectious disease such as COVID-19 is one of the major concerns in Malaysia as it becomes the second killing disease and causes huge number of death and spread to other regions of the world if left unchecked. In developed countries, infectious diseases are often preventable, but lack of medical devices in detecting it makes the death cases increase. The growth of different COVID-19 mutation has given so much challenges in detecting, preventing and curing. This gives motivation to researchers in order to solve this global problem by creating and advancing the detection tools and methods. Time,

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Gopatoti, Anandbabu, and P. Vijayalakshmi. "Optimized chest X-ray image semantic segmentation networks for COVID-19 early detection." Journal of X-Ray Science and Technology 30, no. 3 (2022): 491–512. http://dx.doi.org/10.3233/xst-211113.

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BACKGROUND: Although detection of COVID-19 from chest X-ray radiography (CXR) images is faster than PCR sputum testing, the accuracy of detecting COVID-19 from CXR images is lacking in the existing deep learning models. OBJECTIVE: This study aims to classify COVID-19 and normal patients from CXR images using semantic segmentation networks for detecting and labeling COVID-19 infected lung lobes in CXR images. METHODS: For semantically segmenting infected lung lobes in CXR images for COVID-19 early detection, three structurally different deep learning (DL) networks such as SegNet, U-Net and hybr

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D Gawande, Prajkta, and GD Dalvi. "COVID-19 Detection System Using Image Processing and Biomedical." International Journal of Science and Research (IJSR) 10, no. 5 (2021): 455–58. https://doi.org/10.21275/sr21510120342.

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A, Pavan Narayana, Janardhan Guptha S, Deepak S, and Pujith Sai P. "Smart Door / COVID-19 Face Mask Detection." International Journal of Innovative Technology and Exploring Engineering 10, no. 9 (2021): 87–92. http://dx.doi.org/10.35940/ijitee.i9369.0710921.

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January 27 2020, a day that will be remembered by the Indian people for a few decades, where a deadly virus peeped into a life of a young lady and till now it has been so threatening as it took up the life of 3.26 lakh people just in India. With the start of the virus government has made mandatory to wear masks when we go out in to crowded or public areas such as markets, malls, private gatherings and etc. So, it will be difficult for a person in the entrance to check whether everyone one are entering with a mask, in this paper we have designed a smart door face mask detection to check whether

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Pavan, Narayana A., Guptha S. Janardhan, S. Deepak, and Sai P. Pujith. "Smart Door / COVID-19 Face Mask Detection." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 9 (2021): 87–92. https://doi.org/10.35940/ijitee.I9369.0710921.

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January 27 2020, a day that will be remembered by the Indian people for a few decades, where a deadly virus peeped into a life of a young lady and till now it has been so threatening as it took up the life of 3.26 lakh people just in India. With the start of the virus government has made mandatory to wear masks when we go out in to crowded or public areas such as markets, malls, private gatherings and etc. So, it will be difficult for a person in the entrance to check whether everyone one are entering with a mask, in this paper we have designed a smart door face mask detection to check whether

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Kouamé, Konan-Marcelin, and Hamid Mcheick. "An Ontological Approach for Early Detection of Suspected COVID-19 among COPD Patients." Applied System Innovation 4, no. 1 (2021): 21. http://dx.doi.org/10.3390/asi4010021.

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Recent studies on chronic obstructive pulmonary disease (COPD) patients in the context of the coronavirus 19 (COVID-19) pandemic have reported two important problems, i.e., high mortality and vulnerability among COPD patients vs. non-COPD patients. The high number of deaths are caused by exacerbations, COVID-19, and other comorbidities. Therefore, the purpose of this article is to reduce the risk factors of COPD in the COVID-19 context. In this article, we propose approaches based on adaptation mechanisms for detecting COVID-19 symptoms, to better provide appropriate care to COPD patients. To

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Lu, Heng-yang, Chenyou Fan, Xiaoning Song, and Wei Fang. "A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media." PeerJ Computer Science 7 (August 20, 2021): e688. http://dx.doi.org/10.7717/peerj-cs.688.

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Background Rumor detection is a popular research topic in natural language processing and data mining. Since the outbreak of COVID-19, related rumors have been widely posted and spread on online social media, which have seriously affected people’s daily lives, national economy, social stability, etc. It is both theoretically and practically essential to detect and refute COVID-19 rumors fast and effectively. As COVID-19 was an emergent event that was outbreaking drastically, the related rumor instances were very scarce and distinct at its early stage. This makes the detection task a typical fe

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Rathore, Miss Himanshi. "Detecting Fake Covid 19 News." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2651–55. http://dx.doi.org/10.22214/ijraset.2021.35271.

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The fake news Detection program exists to help its users distinguish between useful information and baseless rumours. It helps one to verify it themselves. In the current coronavirus disease (COVID-19) pandemic, misinformation is particularly prevalent, leading to people believing false and potentially harmful statements and posts. The spread of panic and misunderstanding among the public can be reduced if fake news is detected quickly. This covid 19 fake news detection model is specifically built to identify fake news.

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A. Bida, Falah, and Hadi R. Ali. "Detection of COVID-19 using wavelet transform." AlKadhum Journal of Science 1, no. 2 (2023): 52–58. http://dx.doi.org/10.61710/akjs.v1i2.49.

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Since December 2019, the world has been struggling Against the discovered virus called Covid-19, which Its symptoms are similar to pneumonia. Being highly contagious, it is It spread all over the world, hence the World Health Organization By declaring this disease as a global pandemic. some Patients infected with this virus suffer from severe symptoms And deadly. Hence the importance of early detection of Coronavirus (COVID-19). COVID-19 is a disease that affects the respiratory system of the human body, and detecting this disease is complex and one of the main challenges. This work proposed a

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R, Ashok Kumar, and Akthar Shaheda. "COVID-19 Chest X-Ray Case Detection with Ensemble Deep-Learning." Indian Journal of Science and Technology 17, no. 18 (2024): 1898–905. https://doi.org/10.17485/IJST/v17i18.2220.

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Abstract <strong>Objectives:</strong> The objective of this research is to enhance accuracy on the COVID-19 case identification using X-ray imagery by addressing the drawbacks of utilising a single deep learning model, such as overfitting, high variance, and generalisation errors, by generating predictions with numerous frameworks as opposed to one model. <strong>Methods:</strong> In this study, secondary data sets from a group of experts from Qatar University in Doha, Qatar, and the University of Dhaka in Bangladesh, together with partners from Pakistan and Malaysia, have produ

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S. B, Nithyashree, Ramya Bharathi R, Poe Pyae Hay Thar Monaa. A, and M. Durgadevi. "Covid-19 Face Mask Detection and People Count." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1064–71. http://dx.doi.org/10.22214/ijraset.2022.42377.

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Abstract: During this pandemic the problems faced was shortage of testing services, vaccines and poor medical care all these lead to lockdown for a long run which affected our daily lives. During this pandemic the problems faced were shortage of testing services, vaccine and poor medical care all these lead to lockdown for the long run which affected our daily lives. The prevention for this problem was to wear masks in public areas. Facemask detection had seen remarkable growth in the image processing and deep learning domain. Facemask detection is already an existing concept in which they had

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Albadr, Musatafa Abbas Abbood, Sabrina Tiun, Masri Ayob, Fahad Taha AL-Dhief, Khairuddin Omar, and Faizal Amri Hamzah. "Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection." PLOS ONE 15, no. 12 (2020): e0242899. http://dx.doi.org/10.1371/journal.pone.0242899.

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The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is

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Lee, Min-Fan Ricky, and Yi-Ching Christine Chen. "COVID-19 Pandemic Response Robot." Machines 10, no. 5 (2022): 351. http://dx.doi.org/10.3390/machines10050351.

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Due to an arising COVID-19 positive confirmed case in Taiwan, the screening of body temperature, mask wearing and quarantined violation is enhanced. A mobile robot that conducts this task is demanded to reduce the human labor. However, conventional robots suffer from several limitations, perceptual aliasing (e.g., different places/objects can appear identical), occlusion (e.g., place/object appearance changes between visits), different viewpoints, the scale of objects, low mobility, less functionality, and some environmental limitations. As for the thermal imager, it displays the current heat

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Anisha, Benjamin Chavan, and Vajid Khan Dr. "Facial Mask Detection System based on COVID-19 Protocol." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 2277–80. https://doi.org/10.5281/zenodo.8012088.

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In the midst of the ongoing COVID-19 pandemic, adherence to safety measures such as wearing masks has become crucial. To assist in enforcing maskwearing norms without relying on manual intervention, we present a mask detection system that utilizes a Convolutional Neural Network (CNN) and facial recognition algorithm. Our system compares images of faces with and without masks, automatically detects the presence or absence of masks, and triggers an alert mechanism for the general public. In this report, we address the challenge of detecting masked face regions by proposing a novel approach that

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El Fiky, Ahmed Hashem. "Deep COVID 19: Deep Learning for COVID 19 Detection from X ray Images." International Journal of Innovative Technology and Exploring Engineering 11, no. 1 (2021): 1–6. http://dx.doi.org/10.35940/ijitee.a9589.1111121.

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The COVID-19 will take place for the first time in December 2019 in Wuhan, China. After that, the virus spread all over the world, with over 4.7 million confirmed cases and over 315000 deaths as of the time of writing this report. Radiologists can employ machine learning algorithms developed on radiography pictures as a decision support mechanism to help them speed up the diagnostic process. The goal of this study is to conduct a quantitative evaluation of six off-the-shelf convolutional neural networks (CNNs) for COVID-19 X-ray image analysis. Due to the limited amount of images available for

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Ahmed, Hashem El Fiky. "Deep-COVID-19: Deep Learning for COVID-19 Detection from X-ray Images." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 1 (2021): 1–6. https://doi.org/10.35940/ijitee.A9589.1111121.

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The COVID-19 will take place for the first time in December 2019 in Wuhan, China. After that, the virus spread all over the world, with over 4.7 million confirmed cases and over 315000 deaths as of the time of writing this report. Radiologists can employ machine learning algorithms developed on radiography pictures as a decision support mechanism to help them speed up the diagnostic process. The goal of this study is to conduct a quantitative evaluation of six off-the-shelf convolutional neural networks (CNNs) for COVID-19 X-ray image analysis. Due to the limited amount of images available for

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Lindawati, Lindawati, and Ani Fadmawati. "ASSISTANCE OF STUDENTS IN DETECTING THE RISK OF TRANSMISSION OF COVID-19 AT THE NURUL JADID ORPHANAGE, TANGERANG DISTRICT." Jurnal Menara Pengabmas 1, no. 2 (2023): 6–11. http://dx.doi.org/10.36743/jmp.v1i2.589.

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Early detection of Covid-19 transmission is an effort to suppress the spread of Covid-19. Ministry of Education and Culture, (2021) the transmission of Covid-19 infection now is school children, students who live in dormitories or orphanages so that a new cluster of Covid transmission appears as a result of not implementing health protocols with discipline. In addition, there is a lack of correct knowledge about the dangers of Covid-19, revention and the ability to detect infection or the risk of being exposed to Covid-19. One of the groups that is very at risk of being infected with Covid-19

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Kumar, R. Ashok, and Shaheda Akthar. "COVID-19 Chest X-Ray Case Detection with Ensemble Deep-Learning." Indian Journal Of Science And Technology 17, no. 18 (2024): 1898–905. http://dx.doi.org/10.17485/ijst/v17i18.2220.

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Objectives: The objective of this research is to enhance accuracy on the COVID-19 case identification using X-ray imagery by addressing the drawbacks of utilising a single deep learning model, such as overfitting, high variance, and generalisation errors, by generating predictions with numerous frameworks as opposed to one model. Methods: In this study, secondary data sets from a group of experts from Qatar University in Doha, Qatar, and the University of Dhaka in Bangladesh, together with partners from Pakistan and Malaysia, have produced a dataset of 21,135 CXR pictures for COVID-19 patients

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Smadi, Ahmad AL, Dr Ahed Abugabah, Mutasem K. Al-smadi, and Ahmad Mohammad Al-smadi. "Smart Medical Application of Deep Learning (MUNet) for Detection of COVID-19 from Chest Images." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 1 (2024): 133–53. http://dx.doi.org/10.58346/jowua.2024.i1.010.

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Fighting the outbreak of COVID-19 is now one of humanity's most critical matters. Rapid detection and isolation of infected people are crucial for decelerating the disease's spread. Due to the pandemic, the conventional technique for COVID-19 detection, reverse transcription-polymerase chain reaction, is time-consuming and in small abundance. Therefore, studies have been searching for alternate methods for detecting COVID-19, and thus applying deep learning methods to patients' chest images has been rendering impressive performance. The primary objective of this study is to suggest a technique

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Liu, Yang, Wenfeng Gong, Samuel Clifford, et al. "Designing a multi-layered surveillance approach to detecting SARS-CoV-2: A modelling study." Wellcome Open Research 5 (September 16, 2020): 218. http://dx.doi.org/10.12688/wellcomeopenres.16256.1.

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Background: Countries achieving control of COVID-19 after an initial outbreak will continue to face the risk of SARS-CoV-2 resurgence. This study explores surveillance strategies for COVID-19 containment based on polymerase chain reaction tests. Methods: Using a dynamic SEIR-type model to simulate the initial dynamics of a COVID-19 introduction, we investigate COVID-19 surveillance strategies among healthcare workers, hospital patients, and community members. We estimate surveillance sensitivity as the probability of COVID-19 detection using a hypergeometric sampling process. We identify test

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Perrine, Canivet, Desir Colin, Thys Marie, et al. "The Role of Imaging in the Detection of Non‐COVID‐19 Pathologies during the Massive Screening of the First Pandemic Wave." Diagnostics, no. 12 (June 28, 2022): 1567. https://doi.org/10.3390/ diagnostics12071567.

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During the COVID‐19 pandemic induced by the SARS‐CoV‐2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non‐COVID‐19 chest pathologies discovered de novo. The fortuitous discovery of de novo non‐COVID‐19 lesions was generally not detected by the automated systems for COVID‐19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT b

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Arham Asif, Muhammad Noman, Hassan Yasin, et al. "Role of Immunochromatographic Techniques in the Diagnosis of COVID-19." Indus Journal of Bioscience Research 3, no. 3 (2025): 84–96. https://doi.org/10.70749/ijbr.v3i3.908.

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The rapid and accurate diagnosis of SARS-CoV-2, the virus responsible for COVID-19, is crucial for controlling its spread and managing public health responses. SARS-CoV-2 is an airborne virus transmitted through direct contact or respiratory droplets from infected individuals. While some patients exhibit clear symptoms, others remain asymptomatic, making early detection essential to prevent further transmission. Diagnostic methods for SARS-CoV-2 primarily rely on two strategies: detecting viral RNA and identifying virus-specific antigens or antibodies. Viral RNA detection through reverse trans

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BOUKTIF, Salah, Akib Mohi Ud Din KHANDAY, and Ali OUNI. "Bi-Directional LSTM-Based COVID-19 Detection Using Clinical Reports." Eurasia Proceedings of Science Technology Engineering and Mathematics 23 (October 16, 2023): 209–19. http://dx.doi.org/10.55549/epstem.1365791.

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COVID-19 has affected the entire globe with its rapid spreading, causing a high transmission rate. A huge amount of people come in contact with this deadly virus, and early diagnosis of such kind of viruses may save many lives. This paper proposes an improved approach for detecting COVID-19 based on Long Short Term Memory (LSTM) and taking advantage of early clinical reports. To train the LSTM-based classifier for COVID-19 detection, various preprocessing techniques and word embeddings are employed. These techniques ensure the data is in a suitable format for the LSTM model. The proposed LSTM

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Trzaskowski, Maciej, Anna Mazurkiewicz-Pisarek, Jakub Waldemar Trzciński, et al. "Portable Surface Plasmon Resonance Detector for COVID-19 Infection." Sensors 23, no. 8 (2023): 3946. http://dx.doi.org/10.3390/s23083946.

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Methods based on nucleic acid detection are currently the most commonly used technique in COVID-19 diagnostics. Although generally considered adequate, these methods are characterised by quite a long time-to-result and the necessity to prepare the material taken from the examined person—RNA isolation. For this reason, new detection methods are being sought, especially those characterised by the high speed of the analysis process from the moment of sampling to the result. Currently, serological methods of detecting antibodies against the virus in the patient’s blood plasma have attracted much a

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Kaur, Sarabpreet. "Development of COVID-19 Detection Smart Doorbell Based on IoT." Journal of Advanced Research in Medical Science & Technology 08, no. 04 (2022): 7–10. http://dx.doi.org/10.24321/2394.6539.202114.

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A unique thermal sensor camera must be utilised to detect a person with covid 19 symptoms. A Coronavirus doorbell has been invented in this research, which can be used to uniquely identify persons using their fingerprints, and they can also be identified for COVID positive. This is an electronic based gadget made with a Raspberry Pi 3, an IR sensor, a Serial 16x2 Serial LCD, a Raspberry Pi camera, and tools for fingerprint scanning. A COVID doorbell has been obtained by combining these tools to detect an infected person based on his temperature. When a person’s temperature exceeds 104 degrees

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Niranjan, L., Nadeem Ahmed Aaliyar, Khan Aftab, Abbas Hassan, and Usaid Ahmed Jawad. "Detection of Covid-19 Risk Factors in Real Time using Mask Detection and Body Temperature." Journal of Advances in Computational Intelligence Theory 3, no. 3 (2021): 1–8. https://doi.org/10.5281/zenodo.5712297.

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Due From the starting of this pandemic crises covid-19, they are more than 6 millions of people who has been effected by this covid-19 and still being effected and numbers are rising very rapidly. Detection of this covid-19 disease and healthy precautions are very important in these cases, Especially when there are lot of peoples in a crowd like in some functions or venues where people will be having lots of contacts between each other, to prevent this, we are implementing an automated system for detecting the body temperature and mask detection of a particular person in real time, this can pr

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Dhamad, Ahmed E., and Muna A. Abdal Rhida. "COVID-19: molecular and serological detection methods." PeerJ 8 (October 7, 2020): e10180. http://dx.doi.org/10.7717/peerj.10180.

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Since COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared as a pandemic disease by the World Health Organization in early 2020, many countries, organizations and companies have tried to find the best way to diagnose the virus and contain its spreading. SARS-CoV-2 is a positive-sense single RNA (+ssRNA) coronavirus and mainly spreads through droplets, respiratory secretions, and direct contact. The early detection of the virus plays a central role in lowering COVID19 incidents and mortality rates. Thus, finding a simple, accurate, cheap and quick detec

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Donaldson, Laurie. "New biosensing platform for Covid-19 detection." Materials Today 44 (April 2021): 1. http://dx.doi.org/10.1016/j.mattod.2021.01.024.

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Braca, Paolo, Domenico Gaglione, Stefano Marano, Leonardo Maria Millefiori, Peter Willett, and Krishna R. Pattipati. "Quickest Detection of COVID-19 Pandemic Onset." IEEE Signal Processing Letters 28 (2021): 683–87. http://dx.doi.org/10.1109/lsp.2021.3068072.

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Rajavenkatesswaran, K. C. "Detection of Covid-19 using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 3 (2021): 1273–76. http://dx.doi.org/10.22214/ijraset.2021.33486.

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Sali, Nirmiti, Nimisha Khadilkar, Srushti Biwalkar, Amisha Swamy, and Nusrat Ansari. "COVID-19 Detection using Chest X-Ray." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 278–85. http://dx.doi.org/10.22214/ijraset.2022.41814.

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Abstract: Over the past few months, the exponential increase in COVID-19 cases has been overwhelming for many healthcare systems across the world. With 114 million cases globally as of 28th February 2021, with India itself having 11.1 million cases, it has challenged us with the testing, quarantine, and safety measures. Having limited testing kits, not all patients that have symptoms of respiratory illness can be tested using conventional techniques (RT-PCR). In this project, we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. It would also hel

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Serte, Sertan, Mehmet Alp Dirik, and Fadi Al-Turjman. "Deep Learning Models for COVID-19 Detection." Sustainability 14, no. 10 (2022): 5820. http://dx.doi.org/10.3390/su14105820.

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Healthcare is one of the crucial aspects of the Internet of things. Connected machine learning-based systems provide faster healthcare services. Doctors and radiologists can also use these systems for collaboration to provide better help to patients. The recently emerged Coronavirus (COVID-19) is known to have strong infectious ability. Reverse transcription-polymerase chain reaction (RT-PCR) is recognised as being one of the primary diagnostic tools. However, RT-PCR tests might not be accurate. In contrast, doctors can employ artificial intelligence techniques on X-ray and CT scans for analys

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Taneja, Harsh, Abhinav Abhinav, Apoorv Apoorv, Himanshu Mangal, and Naman Agarwal. "Detection of Covid-19 using Cough Sounds." Fusion: Practice and Applications 7, no. 2 (2022): 79–90. http://dx.doi.org/10.54216/fpa.070202.

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Coronavirus, the pandemic due to which about 4 million have lost their lives and counting, is still on. Many scientists and researchers are trying to find ways to detect coronavirus as soon as possible in the human body so that they can start their medication and precaution as soon as possible. Still, due to lack of lab facilities, the RT-PCR is taking more than three days to give the report, and in the meanwhile, patients get serious and life in danger. So in this paper, we proposed an audio-based coronavirus detection technique in which we can get results in minutes. Coronavirus is a respira

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Tepeli Büyüksünetçi, Yudum, Burak Ekrem Çitil, and Ülkü Anık. "An impedimetric approach for COVID-19 detection." Analyst 147, no. 1 (2022): 130–38. http://dx.doi.org/10.1039/d1an01718g.

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Bai, Xiaoya, Chenghao Li, Yueyan Li, and Yujian Lin. "COVID-19: pathogenic mechanism, detection, and treatment." E3S Web of Conferences 292 (2021): 03092. http://dx.doi.org/10.1051/e3sconf/202129203092.

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COVID-19, which broke out in 2019 in China, is caused by SARS-CoV-2. According to the latest WHO real-time statistics, as of 1:19 p.m. BST on April 22, there were 142557268 cumulative new confirmed cases of coronavirus pneumonia and 3033798 cumulative deaths worldwide. Therefore, it is significant to understand the structure and the pathogenic mechanism of SARS-CoV-2, which is helpful to understand the diagnosis and treatment of sars-cov-2. We primarily investigate related sources on Pubmed and related scholar websites. We found some antiviral drugs such as lopinavir, chloroquine, and its deri

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Prabhu, Shreekanth M., Natarajan Subramanyam, and Rhythm Girdhar. "Containing COVID-19 Pandemic using Community Detection." Journal of Physics: Conference Series 1797, no. 1 (2021): 012008. http://dx.doi.org/10.1088/1742-6596/1797/1/012008.

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