標(biāo)題: Titlebook: Computerized Systems for Diagnosis and Treatment of COVID-19; Joao Alexandre Lobo Marques,Simon James Fong Book 2023 The Editor(s) (if app [打印本頁(yè)] 作者: Constrict 時(shí)間: 2025-3-21 16:10
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19影響因子(影響力)
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19被引頻次
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19被引頻次學(xué)科排名
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19年度引用
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19年度引用學(xué)科排名
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19讀者反饋
書(shū)目名稱(chēng)Computerized Systems for Diagnosis and Treatment of COVID-19讀者反饋學(xué)科排名
作者: hauteur 時(shí)間: 2025-3-21 22:44
https://doi.org/10.1007/978-3-8350-9260-0rm patient infection can be lengthy, and the process is expensive. On the other hand, X-Ray and CT scans play a vital role in the auxiliary diagnosis process. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-di作者: lactic 時(shí)間: 2025-3-22 01:06
https://doi.org/10.1007/978-3-8350-9260-0-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quant作者: 改變立場(chǎng) 時(shí)間: 2025-3-22 07:40 作者: Foreshadow 時(shí)間: 2025-3-22 09:13
https://doi.org/10.1007/978-3-8350-9260-0pecialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the sprea作者: KIN 時(shí)間: 2025-3-22 12:53 作者: KIN 時(shí)間: 2025-3-22 18:32
https://doi.org/10.1007/978-3-8350-9260-0nd healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regre作者: 微生物 時(shí)間: 2025-3-22 23:52
https://doi.org/10.1007/978-3-8350-9260-0k in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the作者: 許可 時(shí)間: 2025-3-23 05:09
https://doi.org/10.1007/978-981-33-4952-0 disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a 作者: FLEET 時(shí)間: 2025-3-23 07:38
https://doi.org/10.1007/978-3-211-49855-2 medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the seve作者: 人類(lèi)的發(fā)源 時(shí)間: 2025-3-23 10:48
https://doi.org/10.1007/978-3-211-49855-2ared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition作者: 角斗士 時(shí)間: 2025-3-23 17:38
https://doi.org/10.1007/978-3-031-30788-1Computerized Diagnostic Support; Artificial Intelligence; Signal and Image Processing; Biofeedback; Covi作者: Offset 時(shí)間: 2025-3-23 20:51 作者: Confess 時(shí)間: 2025-3-24 01:58
Joao Alexandre Lobo Marques,Simon James FongProvides a comprehensive approach for lung segmentation task including X-Ray and CT-Scan images.Describes a‘reliable Covid-19 diagnostic support system based on X-Ray and CT-Scan imaging.Presents the 作者: 陰謀 時(shí)間: 2025-3-24 04:33
http://image.papertrans.cn/c/image/234581.jpg作者: Encapsulate 時(shí)間: 2025-3-24 07:03
Technology Developments to Face the COVID-19 Pandemic: Advances, Challenges, and Trends,need to be continuously studied by the medical and academic communities. According to the World Health Organization (WHO), until January 2023, more than 650 million cases were officially accounted (with probably much more non tested cases) with 6,656,601 deaths officially linked to the COVID-19 as p作者: Acclaim 時(shí)間: 2025-3-24 13:34 作者: 粗魯性質(zhì) 時(shí)間: 2025-3-24 18:31 作者: 人造 時(shí)間: 2025-3-24 20:46 作者: 閑蕩 時(shí)間: 2025-3-25 01:03
X-Ray Machine Learning Classification with VGG-16 for Feature Extraction,pecialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the sprea作者: 萬(wàn)靈丹 時(shí)間: 2025-3-25 03:53
Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers,ns’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Beca作者: CURL 時(shí)間: 2025-3-25 10:26 作者: insular 時(shí)間: 2025-3-25 15:23 作者: 欲望小妹 時(shí)間: 2025-3-25 17:47 作者: 必死 時(shí)間: 2025-3-25 20:16
,Classification of?Severity of?COVID-19 Patients Based on?the?Heart Rate Variability, medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the seve作者: 舊石器時(shí)代 時(shí)間: 2025-3-26 03:05
Exploratory Data Analysis on Clinical and Emotional Parameters of Pregnant Women with COVID-19 Sympared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition作者: WAIL 時(shí)間: 2025-3-26 05:54
Book 2023Covid-19 diagnosis and treatment. The book focuses on two main applications: critical diagnosis requiring high precision and speed, and treatment of symptoms, including those affecting the cardiovascular and neurological systems..The areas discussed in this book range from signal processing, time se作者: Pruritus 時(shí)間: 2025-3-26 09:19 作者: 參考書(shū)目 時(shí)間: 2025-3-26 14:16 作者: 本土 時(shí)間: 2025-3-26 18:10 作者: ANTH 時(shí)間: 2025-3-27 00:40 作者: 反饋 時(shí)間: 2025-3-27 02:54 作者: Maximize 時(shí)間: 2025-3-27 09:22
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.作者: cortex 時(shí)間: 2025-3-27 12:44
,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.作者: GIBE 時(shí)間: 2025-3-27 15:29
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作者: 泥土謙卑 時(shí)間: 2025-3-27 20:45
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 作者: 藝術(shù) 時(shí)間: 2025-3-28 01:21
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作者: penance 時(shí)間: 2025-3-28 05:54
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作者: Bravura 時(shí)間: 2025-3-28 10:11 作者: 存心 時(shí)間: 2025-3-28 10:34
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 作者: 健談的人 時(shí)間: 2025-3-28 15:02 作者: 懸崖 時(shí)間: 2025-3-28 20:18
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作者: 吹牛大王 時(shí)間: 2025-3-29 01:58 作者: 反復(fù)無(wú)常 時(shí)間: 2025-3-29 05:15
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 作者: 消音器 時(shí)間: 2025-3-29 08:31
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作者: forthy 時(shí)間: 2025-3-29 12:21 作者: FLORA 時(shí)間: 2025-3-29 15:57
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
