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

ANTH

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https://doi.org/10.1007/978-3-211-49855-2ight and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.

cortex

,Classification of?Severity of?COVID-19 Patients Based on?the?Heart Rate Variability,ight and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.

GIBE

Book 2023 to the high infection and mortality rates, and the multiple consequences of the virusinfection in the human body, the challenges were vast and enormous. These necessitated the integration of different disciplines to address the problems. As a global response, researchers across academia and industr

泥土謙卑

https://doi.org/10.1007/978-981-16-1899-4systems 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

藝術(shù)

https://doi.org/10.1007/978-3-8350-9260-0oise 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

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https://doi.org/10.1007/978-3-8350-9260-0rated satisfactory accuracy, precision, recall, and specificity performance. On the one hand, the Mobilenet architecture outperformed the other CNNs, achieving excellent results for the evaluated metrics. On the other hand, Squeezenet presented a regular result in terms of recall. In medical diagnos

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https://doi.org/10.1007/978-3-8350-9260-0pproach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract