ABSTRACT
The epidemic Covid-19 has extended to majority of nations. This pandemic is due to a contagious condition 'SARS-CoV-2', was identified by the the International Health association. In order to diagnosis this virus from 2D chest computed tomography (CT) images, we applied three different transfer learning algorithms: $VGG-19, ResNet-152V2$ and a Fine-Tuned version of $ResNet-152V2$. The different transfer learning models are used on three hundred and four exams where 74 are normal cases, 60 are community-acquired pneumonia (CAP) cases and 169 were confirmed corona-virus cases. The best accuracy value is reached by the fine-tuned $ResNet-152v2$ by 75% against 70% for the basic $ResNet-152v2$ and 66% for the $VGG-19$. © 2022 IEEE.
ABSTRACT
An international health crisis has been caused by the widespread COVID-19 epidemic. COVID-19 patient diagnoses are made using deep learning, although this necessitates a massive radiography data collection in order to efficiently deliver an optimum result. This paper presents a novel Intelligent System with IoT sensors for covid 19 and "Bilinear Resnet 18 Deep Greedy Network,"which is effective with a limited amount of datasets. Despite peculiarities brought on by a small dataset, the suggested approach could successfully combat the anomalies of over fitting and under fitting. The suggested architecture ensures a successful conclusion when the trained model is correctly evaluated using the provided X-ray datasets of COVID-19 cases. The recommended model offers accuracy of 97%, which is superior to existing methodologies. Better precision, recall, and F1 score are provided;which are 98%, 96%, and 96.94% respectively, which is better than other existing methodology. © 2022 IEEE.
ABSTRACT
COVID-19 pandemic has led to an international health emergency the WHO considers wearing a face mask an appropriate form of public health protection. This work will describe a face mask identification model that incorporates both deep and traditional machine learning techniques. Parts of the suggested model can be divided into two. Using Resnet50, the initial part of the system is set up for feature extraction. The second component classifies face masks using decision trees, support vector machines (SVMs), and the ensemble approach. The research will focus on three face-masked datasets. The Real-World Masked Face Dataset Includes three datasets: real-world masked faces, simulated faces, and wild faces (LFW). 99.64% of RMFD's SVM classifier is accurate throughout testing.. It achieved a 99.49% accuracy rate in SMFD and a 100% accuracy rate in LFW. © 2022 IEEE.
ABSTRACT
The new COVID-19 disease has swept the world in recent months, causing enormous disruption to social, economic and health systems. Given the diversity of international health systems and conditions differ from one location to another. In all cases, however, it was decided to limit the elective surgical practices considered non-urgent. Plastic surgery departments have also seen a change in their normal business. The aim of this study was to investigate how the pandemic changed the activity of the Plastic Surgery Department of the "San Giovanni di Dio and Ruggi d'Aragona"University Hospital in Salerno (Italy). In particular, starting from the hospital discharge forms for the two-year period 2019-2020, Gender, Age, Date of admission, Date of discharge, Diagnostic Related Group (DRG) weight and Hospital admission procedures for patients were extracted. Statistical analysis and logistic regression were used to compare the activity of 2019, used in this study as a reference, with that of 2020 in the midst of the pandemic. The analysis showed a statically significant reduction in the Length of Stay (LOS), thus improving appropriateness and achieving a reduction in spending. © 2021 ACM.