A high-speed unsupervised hardware architecture for rapid diagnosis of COVID-19

In the diagnosis of COVID-19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time-consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood-count-test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID-19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID-19. This is achieved by combining three algorithms: the K-means algorithm, a novel statistical analysis technique-HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID-19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high-speed and low-cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation. © 2022 John Wiley & Sons Ltd.

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.

Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans

COVID pandemic across the world and the emergence of new variants have intensified the need to identify COVID-19 cases quickly and efficiently. In this paper, a novel dual-mode multi-modal approach is presented to detect a covid patient. This has been done using the combination of image of the chest X-ray/CT scan and the clinical notes provided with the scan. Data augmentation techniques are used to extrapolate the dataset. Five different types of image and text models have been employed, including transfer learning. The binary cross entropy loss function and the adam optimizer are used to compile all of these models. The multi-modal is also tried out with existing pre-trained models such as: VGG16, ResNet50, InceptionResNetV2 and MobileNetV2. The final multi-modal gives an accuracy of 97.8% on the testing data. The study provides a different approach to identifying COVID-19 cases using just the scan images and the corresponding notes. © 2022

A Comprehending Deep Learning Approach for Disease Classification

For medical image processing analysis, deep learning is one of the most popular research subjects. It is subset of machine learning comprising of one or more neural network layers to simulate human behavior of learning and predicting. The purpose of this work is to investigate the application of deep learning models in image processing for disease analysis and medical innovations. The work showcased a generic deep learning model based on convolutional neural networks to classify diseases upon image analysis. To demonstrate the extensive medical use case of proposed model, the results demonstrated classifying pneumonia x-ray images alongside normal chest x-ray images, wrist r-ray pictures able to distinguish between normal and fractured wrists, etc. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face Mask Detector Using Convolutional Neural Networks

A convolutional neural network (CNN) has one or more layers and is mainly used for image processing, classification, segmentation. CNN is commonly used for satellite image capturing or classifying hand written letters and digits. In this particular project, a convolutional neural network is trained to predict whether a person is wearing a mask or not. The training is done by using a set of masked and unmasked images which constitutes the training data. The performance of the trained model is evaluated on the test dataset, and the accuracy of the prediction is observed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lung disease detection using Self-Attention Generative Adversarial Capsule network optimized with sun flower Optimization Algorithm

Lung cancer is the uncontrolled growth of abnormal cells in one or both lungs. This is one of the dangerous diseases. A lot of feature extraction with classification methods were discussed previously regarding this disease, but none of the methods give sufficient results, not only that, those methods have high over fitting problem, as a result, the detection accuracy was minimizing. Therefore, to overcome these issues, a Lung Disease Detection using Self-Attention Generative Adversarial Capsule Network optimized with Sun flower Optimization Algorithm (SA-Caps GAN-SFOA-LDC) is proposed in this manuscript. Initially, NIH chest X-ray image dataset is gathered through Kaggle repository to diagnose the lung disease. Then, the chests X-ray images are pre-processed by using the contrast limited adaptive histogram equalization (CLAHE) filtering method to eliminate the noise and to enhance the image quality. These pre-processed outputs are fed to feature extraction process. In the feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Self-Attention based Generative Adversarial Capsule classifier for detecting the lung disease. The hyper parameters of SA-Caps GAN classifier is optimized using Sun flower Optimization Algorithm. The simulation is implemented in MATLAB. The proposed SA-Caps GAN-SFOA-LDC method attains higher accuracy 21.05%, 33.28%, 30.27%, 29.68%, 32.57% and 44.28%, Higher Precision 30.24%, 35.68%, 32.08%, 41.27%, 28.57% and 34.20%, Higher F-Score 32.05%, 31.05%, 36.24%, 30.27%, 37.59% and 22.05% analyzed with the existing methods, SVM-SMO-LDC, CNN-MOSHO-LDC, XGboost-PSO-LDC respectively. © 2022 Elsevier Ltd

Employer branding under COVID-19 pandemic in Russian companies

An integrated approach to employer branding during COVID-19 pandemic based on employer branding orientation is considered in this article. The empirical study in employer branding was conducted in Russian companies in 2020. The research object was the HR professionals from Russian medium-sized and large companies. Using the data from the descriptive survey, the number of strategic (employer branding orientation, employer branding strategy, employer value proposition) and operational (communication programs, communication channels and content) features in employer branding in Russian companies during COVID-19 pandemic were identified.

Exploring the Influence of Touch Points on Tourist Experiences at Crisis Impacted Destinations

Customer journeys in tourism are becoming more complex, often including multiple touch points that can influence expectations, experiences, and travel behaviors. The management of these different interactions is further complicated if tourist destinations face natural or man-made crises (e.g., financial crises, COVID-19). The current research takes a comprehensive look at how negative word-of-mouth (WOM) shapes pre-consumption expectations that drive actual tourist experiences and subsequent satisfaction behaviors. Using partial least squares structural equation modeling (PLS-SEM), findings from 188 tourists confirm the influence of uncontrollable, negative WOM on destination image. Yet an actual, positive experience negates these negative pre-trip influences. Tourism managers are rewarded with satisfied and loyal tourists in response to creating positive experiences even at crisis impacted destinations. © The Author(s) 2021.

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall. © 2023 CRL Publishing. All rights reserved.

Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks

According to the World Health Organization (WHO), Pneumonia, COVID-19, Tuberculosis, and Pneumothorax are the leading death causes in the world. Coughing, sneezing, fever, and shortness of breath are common symptoms. To detect them, several tests such as molecular tests (RT-PCR), antigen tests, Monteux tuberculin skin test (TST), and complete blood count (CBC) tests are needed. But these are time-consuming processes and have an error rate of 20% and a sensitivity of 80%. So, radiographic tests like computed tomography (CT) and an X-ray are used to identify lung diseases with the help of a physician. But the risk of these lung diseases' diagnoses overlapping features in chest radiographs is a worry with chest X-ray or CT-scan images. To accurately classify one of four diseases with healthy images demands the automation of such a process. There is no method for identifying and categorizing these lung diseases. As a result, we were encouraged to use eight pre-trained convolutional neural networks (CNN) to classify various lung diseases into COVID-19, pneumonia, pneumothorax, tuberculosis, and normal images from the chest X-ray image dataset. This classification process is divided into two phases. In the training phase, the CNNs are trained with the Adam optimizer with a maximum epoch of 30 and a mini-batch size of 32. In the classification phase, these trained networks are used to classify diseases. In both phases, the dataset is color preprocessed, resized, and undergoes data augmentation. For this, we used eight pre-trained CNNs: Alexnet,Darknet-19, Darknet-53, Densenet-201, Googlenet, InceptionResnetV2, MobilenetV2, and Resnet-18. Finally, we concluded that the best one to classify these diseases. Among these networks, Densenet-201, achieved the highest accuracy of 97.2%, 94.28% of sensitivity, and 97.92% of specificity for K=5. For K=10, it achieved 97.49% of accuracy, 95.57% of sensitivity, and 97.96% of specificity and for K=15, achieved 97.01% of accuracy, 96.71% of sensitivity, and 97.17% of specificity. Hence, the proposed method outperformed the existing state-of-the-art methods. Finally, our proposed research could aid clinicians in making quick conclusions concerning lung problems so that treatment can proceed. © 2022 Elsevier Ltd

"Public goods” or "diplomatic tools”: a framing research on Chinese and American media reports regarding Chinese COVID-19 vaccine

This study aims to conduct a framing analysis of Chinese and American media coverage of Chinese COVID-19 vaccine. Since 2021, China exports or donates vaccines to developing countries, arousing the attention of Chinese and American media. The competition between China and the USA over national image extends to the vaccine field. Media framing is a powerful tool to construct a national image. It is worth comparing how Chinese and American media use framings to construct national images on vaccine issues. This study conducts a framing analysis of news coverage in People's Daily (PD) and The New York Times (NYT) on Chinese COVID-19 vaccine from January to March 2021 to determine how the media use specific framings to construct national images. The results show PD favors official action and cooperation frames, constructing the discourse of "public goods” and "cooperation” and shaping a fair and cooperative China image;while NYT favors official action, conflict and skepticism frames, constructing the discourse of "diplomatic tool” and "competition” and shaping a selfish and competitive China image. A positive image is helpful for China to reposition itself in global public opinion and enhance its soft power, while a negative discourse deconstructs the positive China image and counterbalances China's vaccine diplomacy. © 2022 Asian Media Information and Communication Centre.

Deep CNN Based Video Compression With Lung Ultrasound Sample

Video compression and transmission is an ever-growing area of research with continuous development in both software and hardware domain, especially when it comes to medical field. Lung ultra sound (LUS) is identified as one of the best, inexpensive and harmless option to identify various lung disorders including COVID-19. The paper proposes a model to compress and transfer the LUS sample with high quality and less encoding time than the existing models. Deep convolutional neural network is exploited to work on this, as it focusses on content, more than pixels. Here two deep convolutional neural networks, ie, P(prediction)-net and B(bi-directional)-net model are proposed that takes the input as Prediction, Bidirectional frame of existing Group of Pictures and learn. The network is trained with data set of lung ultrasound sample. The trained network is validated to predict the P, B frame from the GOP. The result is evaluated with 23 raw videos and compared with existing video compression techniques. This also shows that deep learning methods might be a worthwhile endeavor not only for COVID-19, but also in general for lung pathologies. The graph shows that the model outperforms the replacement of block-based prediction algorithm in existing video compression with P-net, B-net for lower bit rates. © The Author('s).

COVID-19 AI Inspector

In this paper, we aimed to aid the control measure that is implemented during the COVID-19 in Taiwan. As the virus spreads rapidly throughout the world, the Taiwanese government imposed three restrictions that help Taiwan to control the spread immediately. One of the restrictions that they imposed is to always wear a face mask. To avoid economic breakdown and still consider the general health of the public, Taiwan limits mass gatherings like in the food industry, entertainment, public transport, religious activities, etc. To be able to increase health security during a mass gathering, we developed an AI software to be able to detect people who are properly wearing a face mask, improperly wearing, and not wearing at all. The data that we used is from Kaggle to be able to use and process the data during image recognition, we use a raspberry pi board and camera. With the algorithm we used;we came up with an outstanding system where we could present excellent results due to the detection accuracy.

KNN-WT BASED COVID-19 DETECTION USING CHEST X-RAY BINARY CLASSIFICATION

A novel virus commences in Wuhan China in December 2019. It was named as novel coronavirus (nCovid-19) or severe acute respiratory syndrome corona virus-2 (SARS-CoV-2). Due to its zoonotic nature, it had affected animals as well as human beings. The stated virus is spreading at such a rapid rate that it has razed human lives and the global economy. To aid in such pandemic situation, we have proposed a novel neural network-based model for diagnosing coronavirus from a raw chest X-Ray image. The proposed model uses K-Nearest Neighbor (KNN) for classifying the input image. It will support binary classification i.e., COVID effected X-Ray and normal X-Ray. Several collected input images are initially pre-processed using dual-tree complex wavelet transform (DTCWT). Then, feature extraction is executed using mobilenet architecture. Further, image classification is performed using the KNN based model. Lastly, the output is predicted whether it belongs to the Covid-19 class or normal class. For visualizing the effectiveness of the proposed KNN based classifier, parameters such as accuracy, recall, precision, and F1 score are calculated. A comparison is made by calculating the average of all the parameters with existing techniques. Experimental results showed that the proposed KNN-WT model achieves an accuracy of 99%. It outperformed all the existing algorithms. © 2023 Little Lion Scientific.

Genomic image representation of human coronavirus sequences for COVID-19 detection

Coronavirus (CoV) disease 2019 (COVID-19) is a severe pandemic affecting millions worldwide. Due to its rapid evolution, researchers have been working on developing diagnostic approaches to suppress its spread. This study presents an effective automated approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19, among other human CoV diseases, with high acceptable accuracy. The GIP technique was applied as follows: first, genomic graphical mapping techniques were used to convert the genome sequences into genomic grayscale images. The frequency chaos game representation (FCGR) and single gray-level representation (SGLR) techniques were used in this investigation. Then, several statistical features were obtained from the images to train and test many classifiers, including the k-nearest neighbors (KNN). This study aimed to determine the efficacy of the FCGR (with different orders) and SGLR images for accurately detecting COVID-19, using a dataset containing both partial and complete genome sequences. The results recommended the fourth-order FCGR image as a proper genomic image for extracting statistical features and achieving accurate classification. Furthermore, the results showed that KNN achieved an overall accuracy of 99.39% in detecting COVID-19, among other human CoV diseases, with 99.48% precision, 99.31% sensitivity, 99.47% specificity, 0.99 F1-score, and 0.99 Matthew's correlation coefficient. © 2022 THE AUTHORS

Detection of COVID Using Deep Learning: A Review

As we all are aware of the fact that India's population is increasing expeditiously, automatic diagnosis of diseases is now crucial topic in medical sciences. Coronavirus has expanded massively, and it is among the one of the most frightful and dangerous infection in latest years. The deadly virus was found in China first, and then, it mutated throughout the world. Hence, automated illness identification provides results that are uniform and quick, and thus, mortality rate can be reduced. Most of countries including ours (India) suffers from lack of testing kits whenever new wave of COVID hits. Therefore, many researchers worked on various deep learning based, machine learning-based approaches for diagnosis of this virus using X-rays and CT scans of lungs. So far, it has affected over 50.9 crore people and caused the deaths of 62.2 lakhs people. Here, in this study, comprehensive survey of fifteen studies is presented where various deep learning, and transfer learning approaches are compared for their efficiency and accuracy. The goal of study here is to inspect and analyse various deep learning models including transfer learning models used, also explore the datasets used, preprocessing techniques used, and compare these models to find which model provide us with optimal and best results. The study can help in smooth implementation of the suggested work in future which can be further, then fine-tuned to get the best results possible. Deep learning provides an easy solution to the COVID problem as they perform best in detection and evaluation. It is found during this study that CNN model hybridized with other models provide better accuracy then CNN alone. Ensemble learning methods also improves the accuracy. Also, before training any model dataset acquired need to be preprocessed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A hybrid convolutional neural network model to detect COVID-19 and pneumonia using chest X-ray images

A hybrid convolutional neural network (CNN)-based model is proposed in the article for accurate detection of COVID-19, pneumonia, and normal patients using chest X-ray images. The input images are first pre-processed to tackle problems associated with the formation of the dataset from different sources, image quality issues, and imbalances in the dataset. The literature suggests that several abnormalities can be found with limited medical image datasets by using transfer learning. Hence, various pre-trained CNN models: VGG-19, InceptionV3, MobileNetV2, and DenseNet are adopted in the present work. Finally, with the help of these models, four hybrid models: VID (VGG-19, Inception, and DenseNet), VMI(VGG-19, MobileNet, and Inception), VMD (VGG-19, MobileNet, and DenseNet), and IMD(Inception, MobileNet, and DenseNet) are proposed. The model outcome is also tested using five-fold cross-validation. The best-performing hybrid model is the VMD model with an overall testing accuracy of 97.3%. Thus, a new hybrid model architecture is presented in the work that combines three individual base CNN models in a parallel configuration to counterbalance the shortcomings of individual models. The experimentation result reveals that the proposed hybrid model outperforms most of the previously suggested models. This model can also be used in the identification of diseases, especially in rural areas where limited laboratory facilities are available. © 2022 Wiley Periodicals LLC.

‘Belong anywhere': Focusing on authenticity and the role of Airbnb in the projected destination image

There is little research that analyses the contribution of tourism-related digital platforms, and particularly Airbnb, to the creation and projection of international destinations' images. This study seeks to address this gap by developing a content analysis of the Airbnb Guides to more than 500 global urban neighbourhoods (globalhoods). We analysed Airbnb users' descriptions posted in the period following the Great Recession up to the COVID-19 pandemic. Content analysis shows how Airbnb projects the images of these globalhoods through a narrative based on creating a perception of authenticity but that finally projects a commodified image of destination identities and their communities. © 2022 The Authors. International Journal of Tourism Research published by John Wiley & Sons Ltd.

CoVSeverity-Net: an efficient deep learning model for COVID-19 severity estimation from Chest X-Ray images

Purpose: COVID-19 is not going anywhere and is slowly becoming a part of our life. The World Health Organization declared it a pandemic in 2020, and it has affected all of us in many ways. Several deep learning techniques have been developed to detect COVID-19 from Chest X-Ray images. COVID-19 infection severity scoring can aid in establishing the optimum course of treatment and care for a positive patient, as all COVID-19 positive patients do not require special medical attention. Still, very few works are reported to estimate the severity of the disease from the Chest X-Ray images. The unavailability of the large-scale dataset might be a reason. Methods: We aim to propose CoVSeverity-Net, a deep learning-based architecture for predicting the severity of COVID-19 from Chest X-ray images. CoVSeverity-Net is trained on a public COVID-19 dataset, curated by experienced radiologists for severity estimation. For that, a large publicly available dataset is collected and divided into three levels of severity, namely Mild, Moderate, and Severe. Results: An accuracy of 85.71% is reported. Conducting 5-fold cross-validation, we have obtained an accuracy of 87.82 ± 6.25%. Similarly, conducting 10-fold cross-validation we obtained accuracy of 91.26 ± 3.42. The results were better when compared with other state-of-the-art architectures. Conclusion: We strongly believe that this study has a high chance of reducing the workload of overworked front-line radiologists, speeding up patient diagnosis and treatment, and easing pandemic control. Future work would be to train a novel deep learning-based architecture on a larger dataset for severity estimation. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

Chest X-Ray Image Analysis with ResNet50, SMOTE and SafeSMOTE

Our study extends the analysis of biological information from chest X-ray images to explore the specific patterns, essential basis of classification. Following inference approach, we address the representation of features of medical images and class distribution as a comprehensive study of how symptoms of COVID-19 in chest X-ray images can be detected. The method extracts distinct features using a convolutional neural network that allows exploring hierarchical pattern and gather the patterns for images. The class distribution of images in this work is considered as a significant subject of learning process. We were able to discover symptoms of the disease with less positive samples than the negative samples by implementing the equalization technique, i.e., using a positive samples to generate similar samples. We show the efficiency of our method by contributing classification results on real medical images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.