ELIZA

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683276 occurrences (No.7 in the rank) during 5 years in the PubMed. [cache]

235) The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

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PMID:34869413 DOI:10.3389/fmed.2021.714811

2021 Frontiers in medicine

* Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition.

- Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds-both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

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(1)21 types	(18)3 combinations	(35)2 applications	(52)2 languages,
(2)8 methods	(19)3 contexts	(36)2 areas	(53)2 levels:
(3)8 treatment	(20)3 databases	(37)2 between	(54)2 lipid
(4)7 from	(21)3 definitions	(38)2 cell	(55)2 methodological
(5)6 levels	(22)3 developmental	(39)2 cognitive	(56)2 methods,
(6)6 studies	(23)3 drugs	(40)2 communities	(57)2 modes
(7)5 aspects	(24)3 interventions	(41)2 components	(58)2 movement
(8)5 in	(25)3 models	(42)2 concentrations	(59)2 phenotypes
(9)5 settings	(26)3 populations,	(43)2 conditions	(60)2 physical
(10)4 clinical	(27)3 research	(44)2 consensus	(61)2 probiotics
(11)4 countries	(28)3 scales	(45)2 countries,	(62)2 situations
(12)4 factors	(29)3 surfaces	(46)2 designs	(63)2 stakeholders
(13)4 forms	(30)3 than	(47)2 diagnostic	(64)2 study
(14)4 mechanisms	(31)3 ways	(48)2 environments	(65)2 surface
(15)4 populations	(32)2 DNA	(49)2 ethnic	(66)2 techniques
(16)4 strategies	(33)2 UER	(50)2 fields	(67)2 tissues
(17)3 approaches	(34)2 age	(51)2 global

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--- WordNet output for different --- =>いろいろな, 違った, 異なった, 様々の, 種々の, 異なる, 変わった Overview of adj different The adj different has 5 senses (first 4 from tagged texts) 1. (88) different -- (unlike in nature or quality or form or degree; "took different approaches to the problem"; "came to a different conclusion"; "different parts of the country"; "on different sides of the issue"; "this meeting was different from the earlier one") 2. (41) different -- (distinctly separate from the first; "that's another (or different) issue altogether") 3. (2) different -- (differing from all others; not ordinary; "advertising that strives continually to be different"; "this new music is certainly different but I don't really like it") 4. (1) unlike, dissimilar, different -- (marked by dissimilarity; "for twins they are very unlike"; "people are profoundly different") 5. different -- (distinct or separate; "each interviewed different members of the community") --- WordNet end ---