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
