{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:59:57Z","timestamp":1783007997690,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models\u2014a custom CNN, an Edge Impulse CNN, and a custom LSTM\u2014on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds.<\/jats:p>","DOI":"10.3390\/fi16110391","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T07:51:29Z","timestamp":1729842689000},"page":"391","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Empowering Healthcare: TinyML for Precise Lung Disease Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1343-6329","authenticated-orcid":false,"given":"Youssef","family":"Abadade","sequence":"first","affiliation":[{"name":"System Engineering Laboratory, National School of Applied Siences, Ibn Tofail University of Kenitra, Kenitra B.P 242, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1804-6977","authenticated-orcid":false,"given":"Nabil","family":"Benamar","sequence":"additional","affiliation":[{"name":"School of Technology, Moulay Ismail University of Meknes, Meknes 50050, Morocco"},{"name":"School of Science and Engineering, Al Akhawayn University in Ifrane, P.O. Box 104, Hassan II Avenue, Ifrane 53000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5280-3276","authenticated-orcid":false,"given":"Miloud","family":"Bagaa","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, QC G8Z 4M3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Habiba","family":"Chaoui","sequence":"additional","affiliation":[{"name":"System Engineering Laboratory, National School of Applied Siences, Ibn Tofail University of Kenitra, Kenitra B.P 242, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024, March 31). The Top 10 Causes of Death. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/the-top-10-causes-of-death."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hashoul, D., and Haick, H. (2019). Sensors for detecting pulmonary diseases from exhaled breath. Eur. Respir. Rev., 28.","DOI":"10.1183\/16000617.0011-2019"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s40537-023-00762-z","article-title":"A review on lung disease recognition by acoustic signal analysis with deep learning networks","volume":"10","author":"Sfayyih","year":"2023","journal-title":"J. Big Data"},{"key":"ref_4","first-page":"89","article-title":"Respiratory sound analysis in the era of evidence-based medicine and the world of medicine 2.0","volume":"11","author":"Gass","year":"2018","journal-title":"J. Med. Life"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13405","DOI":"10.1007\/s00500-022-07499-6","article-title":"Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds","volume":"26","author":"Alqudah","year":"2022","journal-title":"Soft Comput."},{"key":"ref_6","first-page":"44","article-title":"Deep learning-based lung sound analysis for intelligent stethoscope","volume":"10","author":"Huang","year":"2023","journal-title":"Mil. Med. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1109\/JBHI.2021.3056916","article-title":"Design and Comparative Performance of a Robust Lung Auscultation System for Noisy Clinical Settings","volume":"25","author":"McLane","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Seah, J.J., Zhao, J., Wang, D.Y., and Lee, H.P. (2023). Review on the advancements of stethoscope types in chest auscultation. Diagnostics, 13.","DOI":"10.3390\/diagnostics13091545"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s13755-024-00283-w","article-title":"Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN","volume":"12","author":"Lella","year":"2024","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tsoukas, V., Boumpa, E., Giannakas, G., and Kakarountas, A. (2021, January 26\u201328). A review of machine learning and tinyml in healthcare. Proceedings of the 25th Pan-Hellenic Conference on Informatics, Volos, Greece.","DOI":"10.1145\/3503823.3503836"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"96892","DOI":"10.1109\/ACCESS.2023.3294111","article-title":"A Comprehensive Survey on TinyML","volume":"11","author":"Abadade","year":"2023","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ooko, S.O., Muyonga Ogore, M., Nsenga, J., and Zennaro, M. (2021, January 7\u201311). TinyML in Africa: Opportunities and Challenges. Proceedings of the 2021 IEEE Globecom Workshops (GC Wkshps), Madrid, Spain.","DOI":"10.1109\/GCWkshps52748.2021.9682107"},{"key":"ref_13","first-page":"1595","article-title":"A review on TinyML: State-of-the-art and prospects","volume":"34","author":"Ray","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100461","DOI":"10.1016\/j.iot.2021.100461","article-title":"TinyML Meets IoT: A Comprehensive Survey","volume":"16","author":"Dutta","year":"2021","journal-title":"Internet Things"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nicolas, C., Naila, B., and Amar, R.C. (2022, January 5\u20138). TinyML Smart Sensor for Energy Saving in Internet of Things Precision Agriculture platform. Proceedings of the 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), Barcelona, Spain.","DOI":"10.1109\/ICUFN55119.2022.9829675"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bhamare, M., Kulkarni, P.V., Rane, R., Bobde, S., and Patankar, R. (2024). Chapter 14\u2014TinyML applications and use cases for healthcare. TinyML for Edge Intelligence in IoT and LPWAN Networks, Academic Press.","DOI":"10.1016\/B978-0-44-322202-3.00019-1"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bamoumen, H., Temouden, A., Benamar, N., and Chtouki, Y. (2022, January 20\u201321). How TinyML Can be Leveraged to Solve Environmental Problems: A Survey. Proceedings of the 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Sakheer, Bahrain.","DOI":"10.1109\/3ICT56508.2022.9990661"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"98450","DOI":"10.1109\/ACCESS.2022.3206782","article-title":"Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review","volume":"10","author":"Diab","year":"2022","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sun, B., Bayes, S., Abotaleb, A.M., and Hassan, M. (2023, January 27\u201331). The Case for tinyML in Healthcare: CNNs for Real-Time On-Edge Blood Pressure Estimation. Proceedings of the 38th ACM\/SIGAPP Symposium on Applied Computing, Tallinn, Estonia.","DOI":"10.1145\/3555776.3577747"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/TNSRE.2019.2911602","article-title":"A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System","volume":"27","author":"Saadeh","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fang, K., Xu, Z., Li, Y., and Pan, J. (2021, January 19\u201321). A Fall Detection using Sound Technology Based on TinyML. Proceedings of the 2021 11th International Conference on Information Technology in Medicine and Education (ITME), Wuyishan, China.","DOI":"10.1109\/ITME53901.2021.00053"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, T., Kuang, L., Li, K., Zeng, J., Herrero, P., and Georgiou, P. (2021, January 22\u201328). Blood Glucose Prediction in Type 1 Diabetes Using Deep Learning on the Edge. Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Republic of Korea.","DOI":"10.1109\/ISCAS51556.2021.9401083"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Risso, M., Burrello, A., Pagliari, D.J., Benatti, S., Macii, E., Benini, L., and Pontino, M. (2021, January 22\u201328). Robust and Energy-Efficient PPG-Based Heart-Rate Monitoring. Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Republic of Korea.","DOI":"10.1109\/ISCAS51556.2021.9401282"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alghamdi, N.S., Zakariah, M., and Karamti, H. (2024). A deep CNN-based acoustic model for the identification of lung diseases utilizing extracted MFCC features from respiratory sounds. Multimedia Tools and Applications, Springer.","DOI":"10.1007\/s11042-024-18703-0"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ullah, A., Khan, M.S., Khan, M.U., and Mujahid, F. (2021, January 13\u201314). Automatic Classification of Lung Sounds Using Machine Learning Algorithms. Proceedings of the 2021 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan.","DOI":"10.1109\/FIT53504.2021.00033"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"122136","DOI":"10.1109\/ACCESS.2022.3223444","article-title":"Mel Frequency Cepstral Coefficient and its Applications: A Review","volume":"10","author":"Abdul","year":"2022","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0165-1684(88)90040-0","article-title":"A short-time Fourier transform","volume":"14","author":"Owens","year":"1988","journal-title":"Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sreeram, A., Ravishankar, U., Sripada, N.R., and Mamidgi, B. (2020, January 14\u201316). Investigating the potential of MFCC features in classifying respiratory diseases. Proceedings of the 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), Paris, France.","DOI":"10.1109\/IOTSMS52051.2020.9340166"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Ord\u00e1s, M.T., Ben\u00edtez-Andrades, J.A., Garc\u00eda-Rodr\u00edguez, I., Benavides, C., and Alaiz-Moret\u00f3n, H. (2020). Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data. Sensors, 20.","DOI":"10.3390\/s20041214"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s13640-017-0213-2","article-title":"Classification of lung sounds using convolutional neural networks","volume":"2017","author":"Aykanat","year":"2017","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pandit, M., Gaur, M.K., Rana, P.S., and Tiwari, A. (2022). Asthma Detection System: Machine and Deep Learning-Based Techniques. Artificial Intelligence and Sustainable Computing, Springer.","DOI":"10.1007\/978-981-19-1653-3"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4008813","DOI":"10.1109\/TIM.2023.3292953","article-title":"RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung Sounds","volume":"72","author":"Roy","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1515\/bmt-2022-0421","article-title":"A low power respiratory sound diagnosis processing unit based on LSTM for wearable health monitoring","volume":"68","author":"Zhou","year":"2023","journal-title":"Biomed. Eng. Tech."},{"key":"ref_35","unstructured":"Harvard (2024, March 31). AI for Good-Healthcare. Available online: https:\/\/harvard-edge.github.io\/cs249r_book\/contents\/ai_for_good\/ai_for_good.html#healthcare."},{"key":"ref_36","unstructured":"Hymel, S., Banbury, C., Situnayake, D., Elium, A., Ward, C., Kelcey, M., Baaijens, M., Majchrzycki, M., Plunkett, J., and Tischler, D. (2023). Edge Impulse: An MLOps Platform for Tiny Machine Learning. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"106913","DOI":"10.1016\/j.dib.2021.106913","article-title":"A dataset of lung sounds recorded from the chest wall using an electronic stethoscope","volume":"35","author":"Fraiwan","year":"2021","journal-title":"Data Brief"},{"key":"ref_38","unstructured":"Arduino (2024, October 13). Nano 33 BLE Sense. Available online: https:\/\/docs.arduino.cc\/hardware\/nano-33-ble-sense\/."},{"key":"ref_39","first-page":"800","article-title":"TensorFlow Lite Micro: Embedded Machine Learning for TinyML Systems","volume":"3","author":"David","year":"2021","journal-title":"Proc. Mach. Learn. Syst."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/11\/391\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:20:15Z","timestamp":1760113215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/16\/11\/391"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"references-count":39,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["fi16110391"],"URL":"https:\/\/doi.org\/10.3390\/fi16110391","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]}}}