Diagnosing The Breathing Sounds as COPD or Asthma

Küçük Resim Yok

Tarih

2022

Yazarlar

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Institute of Electrical and Electronics Engineers Inc.

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

The aim of this research is to classify recorded chest sounds to distinguish among Asthma, Bronchiolitis, Bronchiectasis, COPD, Pneumonia and URTI diseases versus Healthy sound. That is, this paper introduces and challenges a seven- class problem using one of the few publicly available collection of sounds, the Respiratory Sound database from Kaggle. The performance of several deep learning algorithms has been compared and the Convolutional Neural Network architecture resulted in the most successful model. Unlike previous papers which worked on a subset of this database, this work proposes a more comprehensive seven-class challenge to distinguish among all diseases sampled in the database. The performance of several deep-learning algorithms has been compared and the best model is described in detail. © 2022 IEEE.

Açıklama

7th International Conference on Computer Science and Engineering, UBMK 2022 -- 14 September 2022 through 16 September 2022 -- -- 183844

Anahtar Kelimeler

Deep Learning, Diagnosing From Sound, Neural Networks, Convolutional Neural Networks, Database Systems, Deep Learning, Diagnosis, Network Architecture, Breathing Sounds, Bronchiolitis, Convolutional Neural Network, Deep Learning, Diagnosing From Sound, Neural Network Architecture, Neural-Networks, Performance, Respiratory Sounds, Sound Database, Learning Algorithms

Kaynak

Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022

WoS Q Değeri

Scopus Q Değeri

N/A

Cilt

Sayı

Künye

Bağlantı

https://doi.org/10.1109/UBMK55850.2022.9919567
 https://hdl.handle.net/11411/6441

Koleksiyon

Scopus Indexed Publications