Titlebook: Computerized Systems for Diagnosis and Treatment of COVID-19; Joao Alexandre Lobo Marques,Simon James Fong Book 2023 The Editor(s) (if app

健談的人

懸崖

https://doi.org/10.1007/978-981-33-4952-0ws of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested: ?(., ., .) and 1 Multi-cla

吹牛大王

反復(fù)無常

Technology Developments to Face the COVID-19 Pandemic: Advances, Challenges, and Trends,systems based on Artificial Intelligence are in fact ready to effectively help on clinical processes, from the perspective of the model proposed by NASA, Technology Readiness Levels (TRL). Finally, two trends are presented with increased necessity of computerized systems to deal with the Long Covid

消音器

Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks,oise and misinterpretation caused by other structures eventually present in the images. This chapter presents an AI-based system for lung segmentation in X-ray images using a U-net CNN model. The system’s performance was evaluated using metrics such as cross-entropy, dice coefficient, and Mean IoU o

forthy

FLORA

X-Ray Machine Learning Classification with VGG-16 for Feature Extraction,r presented the best performance metrics for Covid-19 classification, achieving 90% accuracy, 97.5% of Specificity, 82.5% of Sensitivity, 89.6% of Geometric mean, and 90% for the AUC metric. On the other hand, the Nearest Centroid (NC) classifier presented poor sensitivity and geometric mean results