Classification of Sounds Indicative of Respiratory Diseases

2019 ◽  
pp. 93-103 ◽  
Author(s):  
Stavros Ntalampiras ◽  
Ilyas Potamitis
Keyword(s):  
Respiratory Diseases

COMPLEXITY-BASED CLASSIFICATION OF THE CORONAVIRUS DISEASE (COVID-19)

Fractals ◽  
2020 ◽  
Vol 28 (05) ◽  
pp. 2050114 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
VLADIMIR V. KULISH
Keyword(s):  
Respiratory Diseases ◽  
Fractal Theory ◽  
Respiratory Illness ◽  
Shannon’S Entropy ◽  
Complex Structures ◽  
Acute Respiratory Illness ◽  
X Ray ◽  
Shannon's Entropy ◽  
Human Lungs

COVID-19 is a pandemic disease, which massively affected human lives in more than 200 countries. Caused by the coronavirus SARS-CoV-2, this acute respiratory illness affects the human lungs and can easily spread from person to person. Since the disease heavily affects human lungs, analyzing the X-ray images of the lungs may prove to be a powerful tool for disease investigation. In this research, we use the information contained within the complex structures of X-ray images between the cases of COVID-19 and other respiratory diseases, whereas the case of healthy lungs is taken as the reference point. To analyze X-ray images, we benefit from the concept of Shannon’s entropy and fractal theory. Shannon’s entropy is directly related to the amount of information contained within the X-ray images in question, whereas fractal theory is used to analyze the complexity of these images. The results, obtained in this study, show that the method of fractal analysis can detect the level of infection among different respiratory diseases and that COVID-19 has the worst effect on the human lungs. In other words, the complexity of X-ray images is proportional to the severity of the respiratory disease. The method of analysis, employed in this study, can be used even further to analyze how COVID-19 progresses in affected patients.

Classification of respiratory diseases based on lung and cough sound using convolutional neural network

2021 ◽  
Vol 150 (4) ◽  
pp. A349-A349
Author(s):  
Dhany Arifianto ◽  
Zanjabila Zanjabila ◽  
Puspita Y. Putri ◽  
Jamiatul Firda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Respiratory Diseases ◽  
Cough Sound

scholarly journals Reliability of the Proposed International Classification of High‐Resolution Computed Tomography for Occupational and Environmental Respiratory Diseases

2009 ◽  
Vol 51 (3) ◽  
pp. 210-222 ◽  
Author(s):  
Narufumi Suganuma ◽  
Yukinori Kusaka ◽  
Kurt G. Hering ◽  
Tapio Vehmas ◽  
Thomas Kraus ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Respiratory Diseases ◽  
International Classification ◽  
High Resolution Computed Tomography

The Influence of Outliers on Discrimination of Chronic Obturative Lung Disease

1988 ◽  
Vol 27 (04) ◽  
pp. 167-176 ◽  
Author(s):  
Ewa Krusmska ◽  
Jerzy Liebhart
Keyword(s):  
Lung Disease ◽  
Sample Size ◽  
Test Data ◽  
Medical Diagnosis ◽  
Respiratory Diseases ◽  
Test Sample ◽  
Training Sample ◽  
Small Samples

SummaryThe paper discusses the influence of outliers on the results of linear and canonical discrimination used to assist medical diagnosis in chronic obturative lung disease. The outliers have been detected by χ2-plots based on unweighted sample means and covariances or their weighted analogues with Huber or Hampel weights. With Hampel weights outliers have been found different from those with both remaining methods. After trimming the 10 percent of the most distant individuals, the discrimination was done for the training sample collected earlier (N′ = 305) and for the test sample (N″ = 53) with the functions obtained from the training sample. The discrimination was performed for subsets of the most discriminative variables. When the sample size was sufficiently large (training sample), the goodness of reclassification was similar for classical functions and functions calculated after trimming. For small samples they differ. For classification of the test data the results obtained after trimming (especially with Hampel weights) are much better. The method may be recommended to be used in the computerized respiratory diseases consulting unit.

Sign in / Sign up

Export Citation Format

Share Document