Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model's performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The model's ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.

Marching Ahead through the Pandemic: Continuing Anesthesia Services in COVID Era-Our Experience from a Tertiary Care Cancer Centre

Introduction This paper aims to provide an overview of the administrative and clinical preparations done in a tertiary care cancer hospital in continuing operation theatre (OT) services through the COVID pandemic.Methods Retrospective data collection, data for the past 1.5 years (COVID period) March 2020 to August 2021 were compared to surgical output for a similar duration of time before the COVID era (September 2018-February 2020).Results A total of 1,022 surgeries were done under anesthesia in the COVID period as against 1,710 surgeries done in a similar time frame in the pre-COVID era. Overall, we saw a 40%drop in the total number of cases. Thorax, abdominal, and miscellaneous surgeries (soft tissue sarcomas, urology, and gyneconcology) saw a maximum fall in numbers;however, head and neck cases saw an increase in numbers during the pandemic. Surgical morbidity and mortality were similar in the COVID and pre-COVID era. No cases of severe COVID infection were reported among the healthcare staff working in OT.Discussion We could successfully continue our anesthesia services with minimal risk to healthcare staff throughout the pandemic by adopting major guidelines in a pragmatic and practical approach with minor changes to suit our setup.

Impact of a Yogic Breathing Technique on the Well-Being of Healthcare Professionals During the COVID-19 Pandemic

BACKGROUND: The COVID-19 pandemic created unprecedented challenges for healthcare providers (HCPs), resulting in stress-related disorders, insomnia, and burnout. Sudarshan Kriya Yoga (SKY), a mind-body intervention, was explored as a tool to positively impact the wellbeing of HCPs during the pandemic. METHODS: A pilot study with a single-arm pre-/post-assessment follow-up design was conducted. SKY was taught to participants in a 4-day online workshop between the months of April and June, 2020. Outcomes related to depression, anxiety, resilience, life satisfaction, and quality of sleep were measured using the following scales: Depression, Anxiety & Stress Scale, Connor- Davidson Resilience Scale, Satisfaction With Life Scale, and Pittsburgh Sleep Quality Index. RESULTS: Ninety-two patients completed the pre-/post- and 40-day assessments. A significant reduction was noted in the outcomes of stress, anxiety, depression, resilience, life satisfaction, and quality of sleep immediately after the program (P < .001). At 40 days of practice, significant improvements in resilience (P = .015) and life satisfaction (P < .001) were noted. CONCLUSIONS: SKY demonstrated a positive impact on the well-being of HCPs, even during the dire stresses of the pandemic, with improvements observed in both physical and mental health parameters. A significant, immediate reduction in stress, anxiety, and depression was noted. In addition, sustained improvements in quality of sleep, satisfaction with life, and resilience were experienced among those who practiced SKY. Interventions like SKY may serve as prudent low-cost, high-impact, easy-to-implement options for lowering stress and burnout among physicians.

Rethinking the Transfer Learning Architecture for Respiratory Diseases and COVID-19 Diagnosis

The emergence of COVID-19 has caused a disastrous scenario worldwide, becoming one of the most acute and deadly diseases in the last century wreaking havoc on the health and lives of countless people. The prevalence rate of COVID-19 is growing significantly every day across the world. One critical step in combating COVID-19 is the capacity to identify infected individuals and place them in special care as soon as possible. Detecting this condition via radiography and radiology images is one of the quickest ways to diagnose patients. Early study has found specific abnormalities in the chest radiographs of infected individuals with COVID-19. Inspired by prior research, we examine the application of transfer learning models to detect COVID-19 patients in X-rays. In this study, an X-ray image collection from patients with common bacterial pneumonia, viral pneumonia, proven COVÍD-19 disease, and normal occurrences was used to diagnose coronavirus disease automatically. A dataset has been used in this experiment comprising 76 image samples showing verified COVID-19 illness, 2786 images showing bacterial pneumonia, 1504 images showing viral pneumonia, and 1583 images showing normal circumstances. The information was gathered from publicly accessible X-ray images. Data augmentation technique is applied to the trained image dataset. Two transfer learning models, namely, VGG 16 and Xception, have been modified in this paper after applying additional layers with the base model. Modified Xception model provides an overall accuracy of 84.82% for Adam optimizer and 78.40% for RMSprop optimizer. Modified VGG 16 model provides an overall accuracy of 84.98% for Adam optimizer and 83.88% for RMSprop optimizer. In addition to accuracy, we show each model’s receiver operating characteristic (ROC) curve, precision, recall, F1-score, and AUC. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review

The outbreak of novel coronavirus disease (COVID-19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and computed tomography (CT) images along with DL methods for classifying COVID-19 affected versus pneumonia. The applications of DL techniques to medical images are further described in terms of image localization, segmentation, registration, and classification leading to COVID-19 detection. The reviews of recent papers indicate that the highest classification accuracy of 99.80% is obtained when InstaCovNet-19 DL method is applied to an X-ray dataset of 361 COVID-19 patients, 362 pneumonia patients and 365 normal people. Furthermore, it can be seen that the best classification accuracy of 99.054% can be achieved when EDL_COVID DL method is applied to a CT image dataset of 7500 samples where COVID-19 patients, lung tumor patients and normal people are equal in number. Moreover, we illustrate the potential DL techniques in drug or vaccine discovery in combating the coronavirus. Finally, we address a number of problems, concerns and future research directions relevant to DL applications for COVID-19. © 2021 MIR Labs. All Rights Reserved.

Forecasting the Spread of COVID-19 and ICU Requirements

Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU / semi-ICU units.

HOW MISUSE OF STATISTICS CAN SPREAD MISINFORMATION: A STUDY OF MISREPRESENTATION OF COVID-19 DATA

This paper investigates various ways in which a pandemic such as the novel coronavirus, could be predicted using different mathematical models. It also studies the various ways in which these models could be depicted using various visualization techniques. This paper aims to present various statistical techniques suggested by the Centres for Disease Control and Prevention in order to represent the epidemiological data. The main focus of this paper is to analyse how epidemiological data or contagious diseases are theorized using any available information and later may be presented wrongly by not following the guidelines, leading to inaccurate representation and interpretations of the current scenario of the pandemic;with a special reference to the Indian Subcontinent

Transportation problem with interval-valued intuitionistic fuzzy sets: impact of a new ranking

To address uncertainty and hesitation of a real-life problem, interval-valued intuitionistic fuzzy sets (IVIFSs) have received increasing interest among researchers and industrialists. In this paper, we present an advanced illustration of IVIFSs using physical distancing during COVID-19 to understand the deep concept of IVIFSs. Due to special feature of an IVIFSs, it finds a better decision of a real-life problem having uncertainty and hesitation. Here some important arithmetic operations between two IVIFSs are also stated. Ranking of IVIFSs is a valuable tool and it is not easy to rank due to its ill-defined membership and non-membership degrees, and same difficulties arise in a wide variety of real-life problems. To tackle these difficulties, we introduce a new ranking function of IVIFSs, and it follows well to the law of trichotomy. And for its superiority, we compare it with some existing ranking functions by taking a suitable example. Furthermore, its applicability are tested on the basis of an IVIFSs. Further, it is very interesting to note that some unpredicted factors such as road condition, diesel prices, traffic condition and weather condition affect to the cost of transportation, and therefore, decision makers encounter uncertainty and hesitation to estimate cost of transportation. To resolve such issues, we consider transportation problem with IVIFSs parameters, and for its solution, a simple computational method is developed and illustrated. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

COVID-19 pandemic: An experience of OBGYN residents at Kathmandu Model Hospital

COVID-19, a novel corona virus has affected the life of each and every individual worldwide. Nepal being the neighborhood country of china, though, we had a late case detection. But, since the month of July this virus has spread in an alarming manner in Nepal. Nepal being one of the developing countries, we lack in equipments, manpower resources and also in treatment centers. Looking into the devastating scenario of Covid 19 in China, Italy, New York, Brazil and our neighboring countries like India, Pakistan and Bangladesh is scary. We wondered how we were going to handle this pandemic if similar circumstances happened in our country too. At the same time being OBGYN residents, we all know pregnancy is very crucial and our patients have faced much difficulties to receive the maternal health services. In this manuscript, we have shared our experience regarding preparedness for COVID-19, management of positive cases and its effect in OBGYN trainee. © 2020, Kathmandu University. All rights reserved.