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The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish COVID-19 precisely in chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., COVID-19 case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to confirm the reliability of the proposed method for identifying the patients with COVID-19 disease from X-ray images. The proposed system was trialed on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and COVID-19 cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%. [ABSTRACT FROM AUTHOR]

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%. [ABSTRACT FROM AUTHOR]

COVID-19 continues to spread and has been declared a global emergency. Individuals with current or past infection should be identified as soon as possible to prevent the spread of disease. Surface-enhanced Raman spectroscopy (SERS) is an analytical technique that has the potential to be used to detect viruses at the site of therapy. In this context, SERS is an exciting technique because it provides a fingerprint for any material. It has been used with many COVID-19 virus subtypes, including Deltacron and Omicron, a novel coronavirus. Moreover, flexible SERS substrates, due to their unique advantages of sensitivity and flexibility, have recently attracted growing research interest in real-world applications such as medicine. Reviewing the latest flexible SERS-substrate developments is crucial for the further development of quality detection platforms. This article discusses the ultra-responsive detection methods used by flexible SERS substrate. Multiplex assays that combine ultra-responsive detection methods with their unique biomarkers and/or biomarkers for secondary diseases triggered by the development of infection are critical, according to this study. In addition, we discuss how flexible SERS-substrate-based ultrasensitive detection methods could transform disease diagnosis, control, and surveillance in the future. This study is believed to help researchers design and manufacture flexible SERS substrates with higher performance and lower cost, and ultimately better understand practical applications. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 coronavirus, also known as the disease-causing agent for COVID-19, is a virulent pathogen that may infect people and certain animals. The global spread of COVID-19 and its emerging variation necessitates the development of rapid, reliable, simple, and low-cost diagnostic tools. Many methodologies and devices have been developed for the highly sensitive, selective, cost-effective, and rapid diagnosis of COVID-19. This review organizes the diagnosis platforms into four groups: imaging, molecular-based detection, serological testing, and biosensors. Each platform's principle, advancement, utilization, and challenges for monitoring SARS-CoV-2 are discussed in detail. In addition, an overview of the impact of variants on detection, commercially available kits, and readout signal analysis has been presented. This review will expand our understanding of developing advanced diagnostic approaches to evolve into susceptible, precise, and reproducible technologies to combat any future outbreak. [ABSTRACT FROM AUTHOR]