{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lungbrn-a-smart-digital-stethoscope-for","title":"LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm","arxiv_id":null,"date":"2019-12-05","proceeding":"IEEE Biomedical Circuits and Systems (BIOCAS) 2019 12","authors":["Yi Ma","Xinzi Xu","Qing Yu","Yuhang Zhang","Yongfu Li","Jian Zhao and Guoxing Wang"],"abstract":"Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately. In the scenarios where there is a severely lacking of skilled medical staff, a basic lung sound classification through a digital stethoscope can be used to provide an immediate diagnostic for respiratory-related diseases such as chronic obstructive pulmonary. In this work, we have developed an improved bi-ResNet deep learning architecture, LungBRN, which uses STFT and wavelet feature extraction techniques to  mprove the accuracy compared to the state-of-the-art works. To ensure a fair evaluation, we have adopted the official benchmark standards and the “train-and-test” dataset splitting method stated in the ICBHI 2017 challenge. As a result, we are able to achieve a performance of 50.16%, which is the best result in terms of accuracy compared to all participating teams from ICBHI 2017.","url_abs":"https://ieeexplore.ieee.org/document/8919021","url_pdf":"https://yongfu-li.github.io/papers/LungBRN_A_Smart_Digital_Stethoscope_for_Detecting_Respiratory_Disease_Using_bi-ResNet_Deep_Learning_Algorithm.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lungbrn-a-smart-digital-stethoscope-for","repo_url":"https://github.com/SJTU-YONGFU-RESEARCH-GRP/Lung-Sound-Classification-System-LungSys-I","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"audio-classification","task_name":"Audio Classification"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"lung-sound-classification","task_name":"Lung Sound Classification"},{"task_slug":"sound-classification","task_name":"Sound Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/audio-classification-on-icbhi-respiratory","task":"Audio Classification","dataset":"ICBHI Respiratory Sound Database","model":"bi-ResNet\n(scratch)","rank_in_archive_order":23,"of":25,"metrics":{"ICBHI Score":"50.16","Sensitivity":"31.10","Specificity":"69.20"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}