Classification of asthma severity levels by wheeze sound analysis

2016 ◽  
Author(s):  
Syamimi Mardiah Shaharum ◽  
Kenneth Sundaraj ◽  
Shazmin Aniza ◽  
Rajkumar Palaniappan ◽  
Khaled Helmy
Keyword(s):  
Asthma Severity ◽  
Sound Analysis ◽  
Severity Levels

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

Classification of chestnuts according to moisture levels using impact sound analysis and machine learning

2018 ◽  
Vol 12 (4) ◽  
pp. 2819-2834 ◽  
Author(s):  
Ferhat Kurtulmuş ◽  
Sencer Öztüfekçi ◽  
İsmail Kavdır
Keyword(s):  
Machine Learning ◽  
Sound Analysis

Characterization Of Atopy And Asthma Severity Levels In Children In Orange County, CA

2012 ◽  
Author(s):  
Christina D. Schwindt ◽  
Timothy Young ◽  
Hyun J. Park ◽  
Bruce Nickerson ◽  
Ralph J. Delfino
Keyword(s):  
Orange County ◽  
Asthma Severity ◽  
Severity Levels

CLASSIFICATION OF ASTHMA SEVERITY AMONG STEROID-NAÏVE PEDIATRIC SUBJECTS PREVIOUSLY RECEIVING SHORT-ACTING BETA2-AGONISTS: DO THE GUIDELINES NEED REVISITING?

CHEST Journal ◽  
2005 ◽  
Vol 128 (4) ◽  
pp. 352S
Author(s):  
Bradley Chipps ◽  
Joseph Spahn ◽  
Christine Sorkness ◽  
Amanda Emmett ◽  
Laura Sutton ◽  
...  
Keyword(s):  
Asthma Severity ◽  
Short Acting

Classification of asthma severity: should the international guidelines be changed?

1998 ◽  
Vol 28 (12) ◽  
pp. 1565-1570 ◽  
Author(s):  
Sawyer ◽  
Miles ◽  
Lewis ◽  
Fitzharris ◽  
Pearce ◽  
...  
Keyword(s):  
Asthma Severity ◽  
International Guidelines

scholarly journals Classification of Asthma Severity in Children

2006 ◽  
Vol 160 (8) ◽  
pp. 844 ◽  
Author(s):  
James W. Stout ◽  
Cynthia M. Visness ◽  
Paul Enright ◽  
Carin Lamm ◽  
Gail Shapiro ◽  
...  
Keyword(s):  
Asthma Severity
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