{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T02:38:35Z","timestamp":1783564715155,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UKRI Research England","award":["101053"],"award-info":[{"award-number":["101053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Medical audio classification for lung abnormality diagnosis is a challenging problem owing to comparatively unstructured audio signals present in the respiratory sound clips. To tackle such challenges, we propose an ensemble model by incorporating diverse deep neural networks with attention mechanisms for undertaking lung abnormality and COVID-19 diagnosis using respiratory, speech, and coughing audio inputs. Specifically, four base deep networks are proposed, which include attention-based Convolutional Recurrent Neural Network (A-CRNN), attention-based bidirectional Long Short-Term Memory (A-BiLSTM), attention-based bidirectional Gated Recurrent Unit (A-BiGRU), as well as Convolutional Neural Network (CNN). A Particle Swarm Optimization (PSO) algorithm is used to optimize the training parameters of each network. An ensemble mechanism is used to integrate the outputs of these base networks by averaging the probability predictions of each class. Evaluated using respiratory ICBHI, Coswara breathing, speech, and cough datasets, as well as a combination of ICBHI and Coswara breathing databases, our ensemble model and base networks achieve ICBHI scores ranging from 0.920 to 0.9766. Most importantly, the empirical results indicate that a positive COVID-19 diagnosis can be distinguished to a high degree from other more common respiratory diseases using audio recordings, based on the combined ICBHI and Coswara breathing datasets.<\/jats:p>","DOI":"10.3390\/s22155566","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T04:59:16Z","timestamp":1658897956000},"page":"5566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs"],"prefix":"10.3390","volume":"22","author":[{"given":"Conor","family":"Wall","sequence":"first","affiliation":[{"name":"Department of Computer and Information Sciences, Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6674-692X","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Royal Holloway University of London, Egham TW20 0EX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghong","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Tongda, Nanjing University of Posts and Telecommunications, Nanjing 210049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4263-7168","authenticated-orcid":false,"given":"Akshi","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wall, C., Young, F., Zhang, L., Phillips, E.J., Jiang, R., and Yu, Y. 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