Explainable attention ResNet18-based model for asthma detection using stethoscope lung sounds
Article
Topaloglu, Ihsan, Barua, Prabal Datta, Yildiz, Arif Metehan, Keles, Tugce, Dogan, Sengul, Baygin, Mehmet, Gul, Huseyin Fatih, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra. 2023. "Explainable attention ResNet18-based model for asthma detection using stethoscope lung sounds." Engineering Applications of Artificial Intelligence. 126. https://doi.org/10.1016/j.engappai.2023.106887
Article Title | Explainable attention ResNet18-based model for asthma detection using stethoscope lung sounds |
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ERA Journal ID | 32032 |
Article Category | Article |
Authors | Topaloglu, Ihsan, Barua, Prabal Datta, Yildiz, Arif Metehan, Keles, Tugce, Dogan, Sengul, Baygin, Mehmet, Gul, Huseyin Fatih, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra |
Journal Title | Engineering Applications of Artificial Intelligence |
Journal Citation | 126 |
Article Number | 106887 |
Number of Pages | 15 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0952-1976 |
1873-6769 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.engappai.2023.106887 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0952197623010710 |
Abstract | This study proposes an accurate asthma detection model using an attention network and machine learning technique. The objective of this study is the automated detection of asthma using an attention network. The lung sounds from 203 subjects involving 767 segments from asthma and 722 segments from healthy subjects were collected using a stethoscope. A novel Attention ResNet18-based deep feature engineering model has been developed in five phases: preprocessing, training the Attention ResNet18 network, extracting deep features, iterative feature selection, and classification using k-nearest neighbor (kNN) or support vector machine (SVM). Gradient-weighted class activation mapping (Grad-CAM) was used to generate heat maps, effectively distinguishing asthma lung sounds from those of normal individuals. By using Grad-CAM, explainable results have been presented. Our proposed model obtained an accuracy of 99.73% using SVM with 10-fold cross-validation, surpassing the performance obtained by previous models. Hence, the developed model has the potential to detect asthma in the real-world scenario. scenario to detect. © 2023 Elsevier Ltd |
Keywords | Asthma detection; Deep feature extraction; Attention mechanism; Lung sound classification; Feature selection |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Ardahan State Hospital, Turkey |
School of Business | |
Ardahan University, Turkiye | |
Firat University, Turkey | |
Erzurum Technical University, Turkey | |
Kafkas University, Turkiye | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore | |
School of Mathematics, Physics and Computing |
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