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Real-time reverse transcriptase-polymerase chain reaction (RT-PCR)-based assays are the gold standard for virus diagnosis. Point-of-care (POC) technologies have shown great progress during this period. Herein, we propose a novel fuchsine dye-loaded polymersome for a colorimetric paper-based dot blot spike protein diagnostic assay for COVID-19 via smartphone-assisted sensing. The prepared platform aimed to create an adaptable tool that competes with traditional nanoparticle-based assays employing gold and silver. Analytical characterization and application of the testing platform showed high sensitivity (10 times better than gold nanoparticles), stability, fast turnaround, and reproducibility. The potential and possibilities demonstrated by the current platform could be observed in its adaptability for different markers and pathologies. In addition, smartphone-assisted sensing emphasizes the ability to use the tool at home by common peoples which can lower the burden on the healthcare facilities and reach more underdeveloped regions. • Facile, quick, and sensitive colorimetric paper-based biosensor for COVID-19. • Platform is based on functionalized dye-loaded polymersome. • Polymersomes were more sensitive than silver-enhanced gold nanoparticles. • Higher stability, repeatability, and reproducibility. • The biosensor is adjustable for other diseases. [ABSTRACT FROM AUTHOR]

Coronavirus Disease-2019 (COVID-19) causes Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) and has opened several challenges for research concerning diagnosis and treatment. Chest X-rays and computed tomography (CT) scans are effective and fast alternatives to detect and assess the damage that COVID causes to the lungs at different stages of the disease. Although the CT scan is an accurate exam, the chest X-ray is still helpful due to the cheaper, faster, lower radiation exposure, and is available in low-incoming countries. Computer-aided diagnostic systems based on Artificial Intelligence (AI) and computer vision are an alternative to extract features from X-ray images, providing an accurate COVID-19 diagnosis. However, specialized and expensive computational resources come across as challenging. Also, it needs to be better understood how low-cost devices and smartphones can hold AI models to predict diseases timely. Even using deep learning to support image-based medical diagnosis, challenges still need to be addressed once the known techniques use centralized intelligence on high-performance servers, making it difficult to embed these models in low-cost devices. This paper sheds light on these questions by proposing the Artificial Intelligence as a Service Architecture (AIaaS), a hybrid AI support operation, both centralized and distributed, with the purpose of enabling the embedding of already-trained models on low-cost devices or smartphones. We demonstrated the suitability of our architecture through a case study of COVID-19 diagnosis using a low-cost device. Among the main findings of this paper, we point out the performance evaluation of low-cost devices to handle COVID-19 predicting tasks timely and accurately and the quantitative performance evaluation of CNN models embodiment on low-cost devices. • A new AIaaS Architecture to deliver AI models for embodiment on low-cost devices. • A case study of COVID-19 to demonstrate the suitability of our AIaaS Architecture. • Evaluation of the computational cost of embedding CNNs in a low-cost device. • Relationship evaluation between the CNN computational cost and the number of parameters. • A short state-of-the-art survey regarding COVID-19 diagnosis methods using edge devices. [ABSTRACT FROM AUTHOR]

Infectious diseases accompanied mankind throughout its existence. However, in the 20th century, with the implementation od mass vaccination, this problem was partially forgotten. It reappeared at the end of the 2019 with the COVID-19 pandemic. The diseases are associated with high mortality, the main causes of which are: respiratory failure, acute respiratory distress syndrome, thrombotic complications, etc. As many centuries ago, the key to fighting a pandemic is to diagnose patients with infections as quickly as possible, isolate them, and implement treatment procedures. In this paper we propose a Platform supporting medics in the fight against epidemic. Unlike alternative systems, the proposed IT Platform will ultimately cover all areas of fighting against COVID-19, from the diagnosis of infection, through treatment, to rehabilitation of post-disease complications. Like most clinical information systems, the Platform is based on Artificial Intelligence, in particular Federated Learning. Also, unlike known solutions, it uses all available historical data of the patient's health and information from real-time mobile diagnostics, using cellular communication and Internet of Things solutions. Such solutions could be helpful in fighting against any future mass infections. [ABSTRACT FROM AUTHOR]

Covid19 an ecumenical pandemic perpetuates to take lakhs of lives and consistently taking its shape as major threat. Skeptically and turmoil in divergent perspectives perpetuate to grow. The most prominent contributing factor to all this is the lack of methodologies to test Covid samples at a more immensely colossal scale. Highly scalable, cost efficacious and flexible diagnosis methodology can contribute greatly towards handling this arduous situation in a more controlled manner. Working towards this the major symptom found among the covid patients is cough. With the avail of Deep learning approaches, this cough is processed to understand the distinctions between the conventional and covid cough. One of the major arduousness to address this quandary is the right amplitude of data to build a deep learning model that can authentically take decisions about the cough recordings. We have extracted some of the recordings from the public platforms and performed deep learning predicated analysis. This gave us the prognostication precision of 94% thus authoritatively mandating a better cough dataset to further carry out the research at a more immensely colossal scale. This paper accommodates as a baseline to cerebrate beyond the customary clinical diagnosis and identify the disease at least in the preliminary in fraction of seconds thus requiring the buildup of covid cough data. [ABSTRACT FROM AUTHOR]

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research. • This paper explores deep learning (DL) based studies on chest x-ray classification. • This systematic review discusses the DL techniques used in the related literature. • We summarise chest x-ray public datasets of pneumonia and COVID-19 conditions. • We suggest criteria to select DL techniques based on different considerations. • This paper presents the open challenges and possible future research directions. [ABSTRACT FROM AUTHOR]

The Coronavirus epidemic first appeared in Wuhan-China on December, 31st, 2019. This has put the world's hospitals, clinics, testing laboratories and health administrations under pressure. As of April, 04th, 2020, the World Health Organization reported more than 167515 confirmed cases in more than 100 countries worldwide. The diagnosis of the epidemic will increase the burden on overburdened testing laboratories. Several screening methods have been proposed in parallel in order to facilitate and, above all, to make rapid diagnosis easier. At this level, X-Ray images seem to be a good accompanying solution for emerging countries to help rapid screening. The solution proposed in this paper consists on a collaborative and smart platform based on the Convolutional Neural Network for Classification and Detection namely VGG16. The platform ensures the fast download of the X-Ray image, with the entry of the patient's personal information followed by the launch of a 5 seconds test. The platform generates, as a result, a PDF file containing all patient information. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

Rapid diagnosis of the Covid-19 disease is the best way to prevent infection. In this paper, it is proposed to use machine learning methods to aid diagnoses quickly Covid-19 and focused on effect of several features on classification accuracy. In the proposed method 746 axial computed tomography (CT) images of the lung; 349 Covid-19 (positives) and 397 non-Covid-19 (negative) are used. Gray-level texture, shape and first order statistical features were extracted from the images. The feature vector for model training is constructed with one feature group or combination of more than one group. We then classified with Support Vector Machine, Random Forest, k-nearest neighbor and XGBoost classifier models. The hyperparameter of the models were controlled by the tuning test. Experimental results obtained with 10-fold cross-validation. The results of cross-validation verified with the additionally independent test. The best overall accuracy was 98.65% with first order statistics features classified with XGBoost. In the gray level features, the best individual results given by GLSZM as 81.25%, and the best combination result is with GLDM, GLRLM and GLSZM features as 85.52%. An important finding of this paper is that, for Covid-19 classification, the shape and first order statistics features are more valuable than gray level features. The proposed results compared with the literature studies under some Covid-19 dataset for accuracy, precision, sensitivity and F1-score metrics. Also, the literature studies which used the different Covid-19 dataset were compared with the proposed study. Our results have the significant superiority when compared with the literature studies. [ABSTRACT FROM AUTHOR]

The detection of the COronaVIrus Disease 2019 (COVID-19) from Computed Tomography (CT) scans has become a very important task in modern medical diagnosis. Unfortunately, typical resolutions of state-of-the-art CT scans are still not adequate for reliable and accurate automatic detection of COVID-19 disease. Motivated by this consideration, in this paper, we propose a novel architecture that jointly affords the Single-Image Super-Resolution (SISR) and the reliable classification problems from Low Resolution (LR) and noisy CT scans. Specifically, the proposed architecture is based on a couple of Twinned Residual Auto-Encoders (TRAE), which exploits the feature vectors and the SR images recovered by a Master AE for performing transfer learning and then improves the training of a "twinned" Follower AE. In addition, we also develop a Task-Aware (TA) version of the basic TRAE architecture, namely the TA-TRAE, which further utilizes the set of feature vectors generated by the Follower AE for the joint training of an additional auxiliary classifier, so to perform automated medical diagnosis on the basis of the available LR input images without human support. Experimental results and comparisons with a number of state-of-the-art CNN/GAN/CycleGAN benchmark SISR architectures, performed by considering × 2 , × 4 , and × 8 super-resolution (i.e., upscaling) factors, support the effectiveness of the proposed TRAE/TA-TRAE architectures. In particular, the detection accuracy attained by the proposed architectures outperforms the corresponding ones of the implemented CNN, GAN and CycleGAN baselines up to 9.0%, 6.5%, and 6.0% at upscaling factors as high as × 8. • High-performance automatic COVID-19 disease detection. • Twinned Residual Auto-Encoders for CT scans classification and super-resolution. • Task-aware architecture for joint CT scans classification and super-resolution. [ABSTRACT FROM AUTHOR]

• The study described the case of a 7-week-old suckling baby infected withSARS-CoV-2 virus. • Only culture isolation allowed the identification of the cytopathogenic agent. • Cell culture still remains the only reference method for emerging viruses. SARS-CoV-2 emerged in China in December 2019 and has now been declared a pandemic by the World Health Organization. This paper described the case of a 7-week-old suckling baby from Italy who was SARS-CoV-2-positive only by the cell culture method, with no clinical suspicion of and/or risk factors for SARS-CoV-2 infection. The baby was referred to hospital, with signs and symptoms of upper respiratory tract infection, before the virus had spread to the province. Nasal and pharyngeal swabs and a nasopharyngeal aspirate were used for conventional and molecular diagnostic assays not including the SARS-CoV-2 virus. Bacteria referred to the resident population were revealed in nasal and pharyngeal swabs. No viruses were detected using both immunofluorescence assay and nucleic acid amplification assays in the nasopharyngeal aspirate. The baby was discharged in good condition after 3 days of hospitalisation. Later, a cytopathic effect on the cell monolayers currently used for respiratory viruses was observed and the viral particles were identified as Coronaviridae by transmission electron microscopy. SARS-CoV-2 was identified by RT-PCR performed both on cell culture and on the stored aliquot of the original sample. The virus isolate was named SARS-Cov-2/human/Parma/1/2020. Cell culture still remains the only reference diagnostic method also for emerging viruses, allowing it to reveal cytopathogenic viruses and demonstrate their infectivity. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) techniques extract the most recognizable features for improving the performance of classification methods for medical applications. In this paper, two intelligent wrapper FS approaches based on a new metaheuristic algorithm named the Snake Optimizer (SO) are introduced. The binary SO, called BSO, is built based on an S-shape transform function to handle the binary discrete values in the FS domain. To improve the exploration of the search space by BSO, three evolutionary crossover operators (i.e., one-point crossover, two-point crossover, and uniform crossover) are incorporated and controlled by a switch probability. The two newly developed FS algorithms, BSO and BSO-CV, are implemented and assessed on a real-world COVID-19 dataset and 23 disease benchmark datasets. According to the experimental results, the improved BSO-CV significantly outperformed the standard BSO in terms of accuracy and running time in 17 datasets. Furthermore, it shrinks the COVID-19 dataset's dimension by 89% as opposed to the BSO's 79%. Moreover, the adopted operator on BSO-CV improved the balance between exploitation and exploration capabilities in the standard BSO, particularly in searching and converging toward optimal solutions. The BSO-CV was compared against the most recent wrapper-based FS methods; namely, the hyperlearning binary dragonfly algorithm (HLBDA), the binary moth flame optimization with Lévy flight (LBMFO-V3), the coronavirus herd immunity optimizer with greedy crossover operator (CHIO-GC), as well as four filter methods with an accuracy of more than 90% in most benchmark datasets. These optimistic results reveal the great potential of BSO-CV in reliably searching the feature space. • Binary Snake Optimizer (BSO) is proposed for feature selection problems. • Greedy Crossover operators are integrated with BSO, referred to as BSO-CV. • BSO and BSO-CV are tested on 23 medical datasets and a COVID-19 real dataset. • Experimental results demonstrate the merits of the proposed BSO-CV. [ABSTRACT FROM AUTHOR]

COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors' knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario. • An automated system to support the diagnosis of COVID-19 patients using EfficientNet. • The proposed method has been validated using 10-fold cross-validation. • An external dataset also has been used for validation. • An accuracy of 99.62% and 96.70% is reported for binary and multi-class classification. [ABSTRACT FROM AUTHOR]

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals' infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease. • Utilizing the movement trajectories of individuals in a city to manage infection disease. • Proposing a learning-based system to assess hazardous places of a contagious disease. • Proposing a hidden Markov model to predict the probability of individuals infection. • Applying the Covid-19 outbreak in an urban environment as a case study. [Display omitted] [ABSTRACT FROM AUTHOR]

Almost 80% of people confronting COVID-19 recover from COVID-19 disease without any particular treatments. They experience heterogeneous symptoms; a wide range of respiratory symptoms, cough, dyspnea, fever, and viral pneumonia. However, some others need urgent intervention and special treatment to get rid of this widespread disease. So far, there isn't any unique drug for the potential treatment of COVID 19. However, some available therapeutic drugs used for other diseases seem beneficial for the COVID-19 treatment. On the other hand, there is a robust global concern for developing an efficient COVID-19 vaccine to control the COVID-19 pandemic sustainably. According to the WHO report, since 8 October 2021, 320 vaccines have been in progress. 194 vaccines are in the pre-clinical development stage that 126 of them are in clinical progression. Here, in this paper, we have comprehensively reviewed the most recent and updated information about coronavirus and its mutations, all the potential therapeutic approaches for treating COVID-19, developed diagnostic systems for COVID- 19 and the available COVID-19 vaccines and their mechanism of action. [Display omitted] [ABSTRACT FROM AUTHOR]

Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing. Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] • We introduce and share a new and large dataset of original CT scans. • We introduce a fully automated system for detecting COVID-19 cases that acts with high accuracy and speed. • We propose a new architecture to improve the classification accuracy of images containing important objects in various scales (especially in small scales), which has shown very good improvement. • We evaluated our model in two ways: one based on single-image classification (tested on more than 7,996 images) and the other one for evaluating the automated diagnosis system (tested on 235 patients and 41,892 images). • We have segmented the infection areas of the CT scan images. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects. Our architecture applies a new feature pyramid network designed for classification problems to the ResNet50V2 model so the model becomes able to investigate different resolutions of the image and do not lose the data of small objects. As the infections of COVID-19 exist in various scales, especially many of them are tiny, using our method helps to increase the classification performance remarkably. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways on Xception, ResNet50V2, and our model. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient condition identification phase, the system correctly identified almost 234 of 245 patients with high speed. Our dataset is accessible at https://github.com/mr7495/COVID-CTset. [ABSTRACT FROM AUTHOR]

Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19's ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine. • We have proposed an Integrated Stacking InstaCovNet-19 model. • InstaCovNet-19 was benchmarked against other states of the art models. • Various pre-processing techniques were employed to boost classification performance. • InstaCovNet-19 performance was superior when compared to existing models. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive. • An optimized deep learning DenseNet121 architecture for the diagnosis of COVID-19 disease is proposed. • The gravitational search optimization (GSA) algorithm was used to select the optimal values for the hyperparameters of the DenseNet121 architecture. • The GSA-DenseNet121-COVID-19 performance was compared to the performance of a CNN architecture called Inception-v3. • The proposed approach achieves a 98% accuracy on the test set. [ABSTRACT FROM AUTHOR]