COVID-19 Identification and Detection from CT-Images using AI Based Ensemble Model

The corona virus infection 2019 (COVID-19) has already reached every corner of the globe, leaving many areas with insufficient access to medical supplies. When contrasted to the RT-PCR test, computed tomography (CT) images are able to provide adequate a diagnosis that is both accurate and quick about COVID-19. In this regard, the focus of this research is on the development of an AI-based prediction classifier for the identification and categorization of COVID-19. Ensembles of DL models will be used in the AIEM-DC method in order to accomplish the method's primary goal of accurate COVID-19 detection and classification. In furthermore, a pretreatment approach that relies on Gaussian filtering (GF) is used in order to get rid of clutter and increase image resolution. In addition, for the purpose of extracting the features, a shark optimization method (SOA) is used, along with an array of deep learning methods. These models include recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). In addition, for the categorization of CT images, an upgraded version of the bat method combined with a multiclass support vector machine (MSVM) architecture is used. The originality of the study is shown by the development of the prediction classifier, which includes optimized parameter tuning of the MSVM model for COVID-19 categorization. The usefulness of the AIEM-DC method was tested using a benchmark CT imaging data set, and the findings indicated the potential generalization ability of the AIEM-DC methodology in comparison to the most current state-of-the-art techniques. © 2022 IEEE.

Deep Learning Based Real-Time COVID Norms Violation Detection System

Corona virus disease-2019 (COVID-2019) has impacted on many social behaviours and has put forth some cautiousness in day-to-today life. Therefore, to remove the barrier of fearful life, it is essential to monitor the preventive guidelines suggested by the world health organization. The very first guideline to be followed is to wear a mask and maintain social distance. In order to implement this in a super populous country like India, the administration used very coercive steps. To aid the administration, this paper provides a simple and easy to implement deep learning technique for the detection and recognition of COVID norm violators. Given an unconstrained/ constrained real-time video, the proposed framework uses YOLOv4 model for person localization, height-width comparison for evaluating social distance, and a customized YOLOv4 model for face mask detection. Once the proposed algorithm localizes the violators, it identifies them using convolutional neural network-based face recognition library. The evaluation metrics on benchmark datasets as well as real-time data are obtained. The proposed framework outperforms existing solutions with mAP (mAP @ 0.50 i.e. Mean Average Precision) of 0.9395 on YOLOv4. Comparison of proposed technique with the existing literature illustrates the better trade-off between accuracy and complexity. © 2022, Ismail Saritas. All rights reserved.

Agile Application of Digital Health Interventions during the COVID-19 Refugee Response.

The intersection of digital health platforms and refugee health in the context of the novel 2019 coronavirus disease (COVID-19) has not yet been explored. We discuss the ability of a novel mobile health (mhealth) platform to be effectively adapted to improve health access for vulnerable displaced populations. In a preliminary analysis of 200 Syrian refugee women, we found positive user feedback and uptake of an mhealth application to increase access to preventive maternal and child health services for Syrian refugees under temporary protection in Turkey. Rapid adaptation of this application was successfully implemented during a global pandemic state to perform symptomatic assessment, disseminate health education, and bolster national prevention efforts. We propose that mhealth interventions can provide an innovative, cost-effective, and user-friendly approach to access the dynamic needs of refugees and other displaced populations, particularly during an emerging infectious disease outbreak.

Reflections on Resilience during the COVID-19 Pandemic: Six Lessons from Working in Resource-Denied Settings.

The 2019 novel coronavirus disease (COVID-19) pandemic highlights the experience of communities in the global South that have grappled with vulnerability and scarcity for decades. In the global North, many frontline workers are now being similarly forced to provide and ration care in unprecedented ways, with minimal guidance. We outline six reflections gained as Western practitioners working in resource-denied settings which inform our current experience with COVID-19. The reflections include the following: managing trauma, remaining flexible in dynamic situations, and embracing discomfort to think bigger about context-specific solutions to collectively build back our systems. Through this contextualized reflection on resilience, we hope to motivate strength and solidarity for providers, patients, and health systems, while proposing critical questions for our response moving forward.