{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T12:52:35Z","timestamp":1789735955208,"version":"4.0.1"},"reference-count":118,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T00:00:00Z","timestamp":1684195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient\u2019s respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology\/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations.<\/jats:p>","DOI":"10.3390\/diagnostics13101748","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T01:52:41Z","timestamp":1684288361000},"page":"1748","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview"],"prefix":"10.3390","volume":"13","author":[{"given":"Alyaa Hamel","family":"Sfayyih","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2736-5582","authenticated-orcid":false,"given":"Ahmad H.","family":"Sabry","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Al-Nahrain University Al Jadriyah Bridge, Baghdad 64074, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shymaa Mohammed","family":"Jameel","sequence":"additional","affiliation":[{"name":"Iraqi Commission for Computers and Informatics, Baghdad 10009, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasri","family":"Sulaiman","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1600-8587","authenticated-orcid":false,"given":"Safanah Mudheher","family":"Raafat","sequence":"additional","affiliation":[{"name":"Department of Control and Systems Engineering, University of Technology, Baghdad 10011, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9071-1329","authenticated-orcid":false,"given":"Amjad J.","family":"Humaidi","sequence":"additional","affiliation":[{"name":"Department of Control and Systems Engineering, University of Technology, Baghdad 10011, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasir Mahmood Al","family":"Kubaiaisi","sequence":"additional","affiliation":[{"name":"Department of Sustainability Management, Dubai Academic Health Corporation, Dubai 4545, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13229","DOI":"10.1109\/ACCESS.2022.3146729","article-title":"Emotion Recognition with Audio, Video, EEG, and EMG: A Dataset and Baseline Approaches","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.compbiomed.2016.05.013","article-title":"Lung sound classification using cepstral-based statistical features","volume":"75","author":"Sengupta","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_3","first-page":"18","article-title":"Introduction to Artificial Intelligence","volume":"60","author":"Callier","year":"2021","journal-title":"Actual. Pharm."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chawla, J., and Walia, N.K. (2022, January 18\u201319). Artificial Intelligence based Techniques in Respiratory Healthcare Services: A Review. Proceedings of the 3rd International Conference on Computing, Analytics and Networks (ICAN), Punjab, India.","DOI":"10.1109\/ICAN56228.2022.10007236"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ghrabli, S., Elgendi, M., and Menon, C. (2022). Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review. Diagnostics, 12.","DOI":"10.3390\/diagnostics12092142"},{"key":"ref_6","unstructured":"(2023, April 18). Lung Sounds: Types and Their Causes and Treatment Options. Available online: https:\/\/www.medicalnewstoday.com\/articles\/lung-sounds."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.artmed.2018.04.008","article-title":"Lung sounds classification using convolutional neural networks","volume":"88","author":"Bardou","year":"2018","journal-title":"Artif. Intell. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"15131","DOI":"10.1007\/s00521-020-04864-0","article-title":"Neural network-based design and evaluation of performance metrics using adaptive line enhancer with adaptive algorithms for auscultation analysis","volume":"32","author":"Rajkumar","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101784","DOI":"10.1016\/j.bspc.2019.101784","article-title":"Least Mean Square (LMS) based neural design and metric evaluation for auscultation signal separation","volume":"59","author":"Sathesh","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3635","DOI":"10.1186\/s40658-021-00376-5","article-title":"AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients","volume":"8","author":"Borrelli","year":"2021","journal-title":"EJNMMI Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.4174\/jkss.2013.84.1.18","article-title":"The value of preoperative lung spirometry test for predicting the operative risk in patients undergoing gastric cancer surgery","volume":"84","author":"Jeong","year":"2013","journal-title":"J. Korean Surg. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Petmezas, G., Cheimariotis, G.A., Stefanopoulos, L., Rocha, B., Paiva, R.P., Katsaggelos, A.K., and Maglaveras, N. (2022). Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function. Sensors, 22.","DOI":"10.3390\/s22031232"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Romero G\u00f3mez, A.F., and Orjuela-Ca\u00f1\u00f3n, A.D. (2021, January 26\u201328). Respiratory Sounds Classification employing a Multi-label Approach. Proceedings of the 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI, Cali, Colombia.","DOI":"10.1109\/ColCACI52978.2021.9469042"},{"key":"ref_14","first-page":"5796","article-title":"Crackle Detection in Lung Sounds Using Transfer Learning and Multi-Input Convolutional Neural Networks","volume":"2021","author":"Nguyen","year":"2021","journal-title":"Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Asatani, N., Kamiya, T., Mabu, S., and Kido, S. (2021, January 12\u201315). Classification of Respiratory Sounds by Generated Image and Improved CRNN. Proceedings of the 21st International Conference on Control, Automation and Systems (ICCAS), Jeju, Republic of Korea.","DOI":"10.23919\/ICCAS52745.2021.9649906"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, A.F.R., and Orjuela-Ca\u00f1\u00f3n, A.D. (2022, January 27\u201329). Multilabel and Multiclass Approaches Comparison for Respiratory Sounds Classification. Proceedings of the Communications in Computer and Information Science, Cali, Colombia.","DOI":"10.1007\/978-3-030-91308-3_4"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, L., Cheng, J., Liu, J., Kuang, H., Wu, F., and Wang, J. (2021, January 9\u201312). ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention. Proceedings of the EEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA.","DOI":"10.1109\/BIBM52615.2021.9669787"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Naqvi, S.Z.H., and Choudhry, M.A. (2020). An automated system for classification of chronic obstructive pulmonary disease and pneumonia patients using lung sound analysis. Sensors, 20.","DOI":"10.3390\/s20226512"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bbe.2020.11.003","article-title":"Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers","volume":"41","author":"Fraiwan","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_20","unstructured":"(2022, June 24). ICBHI 2017 Challenge|ICBHI Challenge. (n.d.). Available online: https:\/\/bhichallenge.med.auth.gr\/ICBHI_2017_Challenge."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hsu, F.S., Huang, S.R., Huang, C.W., Huang, C.J., Cheng, Y.R., Chen, C.C., Hsiao, J., Chen, C.W., Chen, L.C., and Lai, Y.C. (2021). Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a selfdeveloped open-access lung sound database-HF_Lung_V1. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0254134"},{"key":"ref_22","first-page":"59","article-title":"Multimedia Respiratory Database (RespiratoryDatabase@TR): Auscultation Sounds and Chest X-rays","volume":"2","author":"Altan","year":"2017","journal-title":"Nat. Eng. Sci."},{"key":"ref_23","unstructured":"Altan, D., and Kutlu, Y. (2023, March 27). RespiratoryDatabase@TR (COPD Severity Analysis). 2020, 1. Available online: https:\/\/data.mendeley.com\/datasets\/p9z4h98s6j\/1."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"17103","DOI":"10.1007\/s00521-021-06302-1","article-title":"ALSD-Net: Automatic lung sounds diagnosis network from pulmonary signals","volume":"33","author":"Baghel","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1109\/TBME.2017.2717280","article-title":"Computerized Lung Sound Screening for Pediatric Auscultation in Noisy Field Environments","volume":"65","author":"Emmanouilidou","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Oweis, R.J., Abdulhay, E.W., Khayal, A., and Awad, A. (2015). An alternative respiratory sounds classification system utilizing artificial neural networks. Biomed. J., 38.","DOI":"10.4103\/2319-4170.137773"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1016\/j.compbiomed.2009.06.011","article-title":"Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes","volume":"89","author":"Bahoura","year":"2009","journal-title":"Comput. Biol. Med."},{"key":"ref_28","unstructured":"(2022, June 24). The, R.A.L.E. Repository. Available online: http:\/\/www.rale.ca\/."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Adhi Pramono, R.X., Imtiaz, S.A., and Rodriguez-Villegas, E. (2019). Evaluation of features for classification of wheezes and normal respiratory sounds. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0213659"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1097\/00129191-200307000-00011","article-title":"Lung Sounds 3.0","volume":"5","author":"Owens","year":"2003","journal-title":"J. Hosp. Palliat. Nurs."},{"key":"ref_31","unstructured":"(2022, June 24). Respiratory Sounds Classification|CS 7651\u2014Machine Learning (Team 7). (n.d.). Available online: https:\/\/fereshtehshah.github.io\/Respiratory_Disorders."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Palaniappan, R., Sundaraj, K., and Sundaraj, S. (2014). A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC Bioinform., 15.","DOI":"10.1186\/1471-2105-15-223"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"195","DOI":"10.35940\/ijeat.A3207.1011121","article-title":"Classification of Lung Sounds and Disease Prediction using Dense CNN Network","volume":"11","author":"Lakhani","year":"2021","journal-title":"Int. J. Eng. Adv. Technol."},{"key":"ref_34","unstructured":"(2022, June 25). Respiratory Sound Database|Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/vbookshelf\/respiratory-sound-database."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.dsp.2014.02.001","article-title":"Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds","volume":"28","year":"2014","journal-title":"Digit. Signal Process. A Rev. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1007\/s40313-021-00727-8","article-title":"Development of Optimal Feature Selection and Deep Learning Toward Hungry Stomach Detection Using Audio Signals","volume":"32","author":"Maria","year":"2021","journal-title":"J. Control. Autom. Electr. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"89029","DOI":"10.1109\/ACCESS.2021.3088949","article-title":"A Multi-Resolution CRNN-Based Approach for Semi-Supervised Sound Event Detection in DCASE 2020 Challenge","volume":"9","author":"Ramos","year":"2021","journal-title":"IEEE Access"},{"key":"ref_38","unstructured":"Gerhard, D. (2003). Audio Signal Classification: History and Current Techniques, Department of Computer Science, University of Regina."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107020","DOI":"10.1016\/j.apacoust.2019.107020","article-title":"Trends in audio signal feature extraction methods","volume":"158","author":"Sharma","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_40","first-page":"5511","article-title":"Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning","volume":"71","author":"Ayvaz","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_41","unstructured":"Ajibola Alim, S., and Khair Alang Rashid, N. (2018). From Natural to Artificial Intelligence\u2014Algorithms and Applications, IntechOpen."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.17654\/EC017051065","article-title":"Comparison of discrete wavelet transform and wavelet packet decomposition for the lung sound classification","volume":"25","author":"Rizal","year":"2017","journal-title":"Far East J. Electron. Commun."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10867-021-09567-8","article-title":"Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: A machine learning approach","volume":"47","author":"Sreejyothi","year":"2021","journal-title":"J. Biol. Phys."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Naqvi, S.Z.H., Arooj, M., Aziz, S., Khan, M.U., Choudhary, M.A., and Ul Hassan, M.N. (2020, January 12\u201313). Spectral Analysis of Lungs sounds for Classification of Asthma and Pneumonia Wheezing. Proceedings of the International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey.","DOI":"10.1109\/ICECCE49384.2020.9179417"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"13132","DOI":"10.3390\/s150613132","article-title":"Using K-nearest neighbor classification to diagnose abnormal lung sounds","volume":"15","author":"Chen","year":"2015","journal-title":"Sensors"},{"key":"ref_46","first-page":"3528","article-title":"ELM and K-nn machine learning in classification of Breath sounds signals","volume":"10","author":"Neili","year":"2020","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s10916-019-1388-0","article-title":"Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: A Risk Stratification Approach in Machine Learning Paradigm","volume":"43","author":"Haider","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"191","DOI":"10.18201\/ijisae.2020466312","article-title":"Identification of the english accent spoken in different countries by the k-nearest neighbor method","volume":"8","author":"Taspinar","year":"2020","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s13755-019-0091-3","article-title":"Convolutional neural networks based efficient approach for classification of lung diseases","volume":"8","author":"Demir","year":"2020","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Falah, A.H., and Jondri, J. (2019, January 24\u201326). Lung sounds classification using stacked autoencoder and support vector machine. Proceedings of the 7th International Conference on Information and Communication Technology (ICoICT), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICoICT.2019.8835278"},{"key":"ref_51","first-page":"143","article-title":"Classification of Pulmonary Crackle and Normal Lung Sound Using Spectrogram and Support Vector Machine","volume":"55","author":"Rizal","year":"2022","journal-title":"J. Biomim. Biomater. Biomed. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Azmy, M.M. (2015, January 3\u20135). Classification of lung sounds based on linear prediction cepstral coefficients and support vector machine. Proceedings of the IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Amman, Jordan.","DOI":"10.1109\/AEECT.2015.7360527"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Phani Sudershan, C., and Narayana Rao, S.V.N. (2020). Classification of crackle sounds using support vector machine. Mater. Today Proc., 81.","DOI":"10.1016\/j.matpr.2020.10.463"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Mayorga, P., Ibarra, D., Zeljkovic, V., and Druzgalski, C. (2015, January 20\u201324). Quartiles and Mel Frequency Cepstral Coefficients vectors in Hidden Markov-Gaussian Mixture Models classification of merged heart sounds and lung sounds signals. Proceedings of the International Conference on High Performance Computing & Simulation (HPCS), Amsterdam, The Netherlands.","DOI":"10.1109\/HPCSim.2015.7237053"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Maruf, S.O., Azhar, M.U., Khawaja, S.G., and Akram, M.U. (2015, January 18\u201320). Crackle separation and classification from normal Respiratory sounds using Gaussian Mixture Model. Proceedings of the IEEE 10th International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka.","DOI":"10.1109\/ICIINFS.2015.7399022"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"32845","DOI":"10.1109\/ACCESS.2019.2903859","article-title":"Triple-Classification of Respiratory Sounds Using Optimized S-Transform and Deep Residual Networks","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1097\/MCP.0000000000000459","article-title":"Artificial intelligence in diagnosis of obstructive lung disease","volume":"24","author":"Das","year":"2018","journal-title":"Curr. Opin. Pulm. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.12928\/telkomnika.v19i4.18874","article-title":"Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique","volume":"19","author":"Foeady","year":"2021","journal-title":"Telkomnika"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"17186","DOI":"10.1038\/s41598-021-96724-7","article-title":"Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning","volume":"11","author":"Kim","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/s41598-018-36454-5","article-title":"A Novel Method for Automatic Identification of Breathing State","volume":"9","author":"Niu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_61","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_62","unstructured":"ul Hassan, M. (2023, March 27). VGG16\u2014Convolutional Network for Classification and Detection. Neurohive. Available online: https:\/\/neurohive.io\/en\/popular-networks\/vgg16\/."},{"key":"ref_63","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Alex Net. Adv. Neural Inf. Process. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhang, C., Benz, P., Argaw, D.M., Lee, S., Kim, J., Rameau, F., Bazin, J.C., and Kweon, I.S. (2021, January 3\u20138). ResNet or DenseNet? Introducing dense shortcuts to ResNet. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00359"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3376922","article-title":"Inception U-Net Architecture for Semantic Segmentation to Identify Nuclei in Microscopy Cell Images","volume":"16","author":"Punn","year":"2020","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s13246-020-00888-x","article-title":"Truncated inception net: COVID-19 outbreak screening using chest X-rays","volume":"43","author":"Das","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"105395","DOI":"10.1016\/j.cmpb.2020.105395","article-title":"DENSE-INception U-net for medical image segmentation","volume":"192","author":"Zhang","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3499","DOI":"10.1109\/TBME.2019.2906667","article-title":"Dilated-inception net: Multi-scale feature aggregation for cardiac right ventricle segmentation","volume":"66","author":"Li","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., and Ha, P. (1998). LeNet. Proc. IEEE, 86.","DOI":"10.1109\/5.726791"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"85","DOI":"10.14311\/NNW.2020.30.007","article-title":"Underwater acoustic signal analysis: Preprocessing and classification by deep learning","volume":"30","author":"Wu","year":"2020","journal-title":"Neural Netw. World"},{"key":"ref_72","first-page":"519","article-title":"Emphysema detection and classification using a multi-scale deep Convolutional Neural Network","volume":"2018","author":"Bermejo","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_73","unstructured":"Zhao, Z., Yang, Z., Luo, L., Zhang, Y., Wang, L., Lin, H., and Wang, J. (2016, January 15\u201318). ML-CNN: A novel deep learning based disease named entity recognition architecture. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"195","DOI":"10.7150\/ijbs.29863","article-title":"A lung sound category recognition method based on wavelet decomposition and BP neural network","volume":"15","author":"Shi","year":"2019","journal-title":"Int. J. Biol. Sci."},{"key":"ref_75","unstructured":"Liu, Y., Zhang, C., and Peng, Y. (2006). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Syahputra, M.F., Situmeang, S.I.G., Rahmat, R.F., and Budiarto, R. (2016, January 26\u201325). Noise reduction in breath sound files using wavelet transform based filter. Proceedings of the International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Semarang, Indonesia.","DOI":"10.11591\/eecsi.v3.1148"},{"key":"ref_77","first-page":"769","article-title":"Feature extraction and classification of lung sounds based on wavelet packet multiscale analysis","volume":"29","author":"Liu","year":"2006","journal-title":"Jisuanji Xuebao Chin. J. Comput."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1513\/AnnalsATS.202001-068OC","article-title":"Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis","volume":"18","author":"Shaish","year":"2021","journal-title":"Ann. Am. Thorac. Soc."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Wu, Y.C., Han, C.C., Chang, C.S., Chang, F.L., Chen, S.F., Shieh, T.Y., Chen, H.M., and Lin, J.Y. (2022). Development of an Electronic Stethoscope and a Classification Algorithm for Cardiopulmonary Sounds. Sensors, 22.","DOI":"10.3390\/s22114263"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"875","DOI":"10.11591\/eei.v8i3.1517","article-title":"Distinctive features for normal and crackles respiratory sounds using cepstral coefficients","volume":"8","author":"Johari","year":"2019","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Wang, Z., Dou, Y., and Zhou, J. (2022). Whispered Speech Conversion Based on the Inversion of Mel Frequency Cepstral Coefficient Features. Algorithms, 15.","DOI":"10.3390\/a15020068"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Mohammadi, M., and Sadegh Mohammadi, H.R. (2017, January 2\u20134). Robust features fusion for text independent speaker verification enhancement in noisy environments. Proceedings of the Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran.","DOI":"10.1109\/IranianCEE.2017.7985357"},{"key":"ref_83","first-page":"585","article-title":"Standardization of computerized respiratory sound analysis","volume":"10","author":"Vanderschoot","year":"2000","journal-title":"Eur. Respir. Rev."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2019.02.068","article-title":"Deep reinforcement learning with its application for lung cancer detection in medical Internet of Things","volume":"97","author":"Liu","year":"2019","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"101886","DOI":"10.1016\/j.compmedimag.2021.101886","article-title":"On the performance of lung nodule detection, segmentation and classification","volume":"89","author":"Gu","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_86","first-page":"28","article-title":"What can artificial intelligence teach us about the molecular mechanisms underlying disease?","volume":"1","author":"Cook","year":"2019","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"5356","DOI":"10.1016\/j.eswa.2015.02.005","article-title":"Image based computer aided diagnosis system for cancer detection","volume":"42","author":"Lee","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3390\/ai1010003","article-title":"Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans","volume":"1","author":"Riquelme","year":"2020","journal-title":"AI"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.21037\/tlcr-20-708","article-title":"Radiomics and artificial intelligence in lung cancer screening","volume":"10","author":"Binczyk","year":"2021","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Espinoza, J.L., and Dong, L.T. (2020). Artificial intelligence tools for refining lung cancer screening. J. Clin. Med., 9.","DOI":"10.3390\/jcm9123860"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"3961","DOI":"10.1007\/s00259-021-05242-1","article-title":"Structural and functional radiomics for lung cancer","volume":"48","author":"Wu","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"105376","DOI":"10.1109\/ACCESS.2020.3000111","article-title":"Classification of Lung Sounds with CNN Model Using Parallel Pooling Structure","volume":"8","author":"Demir","year":"2020","journal-title":"IEEE Access"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/978-981-10-7419-6_8","article-title":"An automated lung sound preprocessing and classification system based onspectral analysis methods","volume":"66","author":"Serbes","year":"2018","journal-title":"IFMBE Proc."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1109\/TBME.2015.2403616","article-title":"A Comparison of SVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds","volume":"62","author":"Sen","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Saraiva, A.A., Santos, D.B.S., Francisco, A.A., Moura Sousa, J.V., Fonseca Ferreira, N.M., Soares, S., and Valente, A. (2020, January 24\u201326). Classification of respiratory sounds with convolutional neural network. Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies\u2014BIOINFORMATICS, Valletta, Malta.","DOI":"10.5220\/0008965101380144"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Yang, Z., Liu, S., Song, M., Parada-Cabaleiro, E., and Schuller, B.W. (2020). Adventitious respiratory classification using attentive residual neural networks. Proc. Interspeech, 2912\u20132916.","DOI":"10.21437\/Interspeech.2020-2790"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Ma, Y., Xu, X., Yu, Q., Zhang, Y., Li, Y., Zhao, J., and Wang, G. (2019, January 17\u201319). Lungbrn: A smart digital stethoscope for detecting respiratory disease using bi-resnet deep learning algorithm. Proceedings of the IEEE Biomedical Circuits and Systems Conference (BioCAS), Nara, Japan.","DOI":"10.1109\/BIOCAS.2019.8919021"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2938","DOI":"10.1109\/JBHI.2021.3064237","article-title":"CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection","volume":"25","author":"Pham","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Gairola, S., Tom, F., Kwatra, N., and Jain, M. (2021, January 1\u20135). RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting. Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Guadalajara, Mexico.","DOI":"10.1109\/EMBC46164.2021.9630091"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Liu, R., Cai, S., Zhang, K., and Hu, N. (2019, January 21\u201324). Detection of Adventitious Respiratory Sounds based on Convolutional Neural Network. Proceedings of the International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Shangai, China.","DOI":"10.1109\/ICIIBMS46890.2019.8991459"},{"key":"ref_101","first-page":"535","article-title":"Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning","volume":"14","author":"Acharya","year":"2020","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.bspc.2018.05.014","article-title":"Deep learning with 3D-second order difference plot on respiratory sounds","volume":"45","author":"Altan","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_103","unstructured":"Kochetov, K., Putin, E., Balashov, M., Filchenkov, A., and Shalyto, A. (2018). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, Proceedings of the 27th International Conference on Artificial Neural Networks, Rhodes, Greece, 4\u20137 October 2018, Springer."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Minami, K., Lu, H., Kim, H., Mabu, S., Hirano, Y., and Kido, S. (2019, January 15\u201318). Automatic Classification of Large-Scale Respiratory Sound Dataset Based on Convolutional Neural Network. Proceedings of the International Conference on Control, Automation and Systems, Jeju, Republic of Korea.","DOI":"10.23919\/ICCAS47443.2019.8971689"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Chambres, G., Hanna, P., and Desainte-Catherine, M. (2018, January 4\u20136). Automatic detection of patient with respiratory diseases using lung sound analysis. Proceedings of the International Workshop on Content-Based Multimedia Indexing, La Rochelle, France.","DOI":"10.1109\/CBMI.2018.8516489"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Jakovljevi\u0107, N., and Lon\u010dar-Turukalo, T. (2018, January 4\u20136). Hidden Markov model based respiratory sound classification. Proceedings of the IFMBE, La Rochelle, France.","DOI":"10.1007\/978-981-10-7419-6_7"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Ma, Y., Xu, X., and Li, Y. (2020). LungRN+NL: An improved adventitious lung sound classification using non-local block resnet neural network with mixup data augmentation. Proc. Interspeech, 2902\u20132906.","DOI":"10.21437\/Interspeech.2020-2487"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"2872","DOI":"10.1109\/TBME.2022.3156293","article-title":"Lung Sound Classification Using Co-tuning and Stochastic Normalization","volume":"69","author":"Nguyen","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_109","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_110","doi-asserted-by":"crossref","unstructured":"Chamberlain, D., Kodgule, R., Ganelin, D., Miglani, V., and Fletcher, R.R. (2016, January 16\u201320). Application of semi-supervised deep learning to lung sound analysis. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590823"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"105162","DOI":"10.1016\/j.cmpb.2019.105162","article-title":"Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images","volume":"185","author":"Rostami","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_112","first-page":"27894647","article-title":"Lung-Deep: A Computerized Tool for Detection of Lung Nodule Patterns using Deep Learning Algorithms Detection of Lung Nodules Patterns","volume":"8","author":"Abbas","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1148\/radiol.2020200165","article-title":"Deep Learning\u2013based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs","volume":"296","author":"Jang","year":"2020","journal-title":"Radiology"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1148\/radiol.2020201240","article-title":"Performance of a deep learning algorithm compared with radiologic interpretation for lung cancer detection on chest radiographs in a health screening population","volume":"297","author":"Lee","year":"2020","journal-title":"Radiology"},{"key":"ref_115","unstructured":"Brain, G. (2021). TensorFlow Hub, TensorFlow."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"104555","DOI":"10.1016\/j.bspc.2022.104555","article-title":"Cochleogram-based adventitious sounds classification using convolutional neural networks","volume":"82","author":"Mang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"39185","DOI":"10.1007\/s11042-022-12958-1","article-title":"Improvise approach for respiratory pathologies classification with multilayer convolutional neural networks","volume":"81","author":"Borwankar","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Varghese, G., Mathan, M., Kumar, S.V., Joseph, J., Raphel, F., and George, R. (2022, January 18\u201320). Automated Detection and Classification of Lung Sound Using 3-D Printed Stethoscope. Proceedings of the 2022 IEEE 6th Conference on Information and Communication Technology, CICT 2022, Gwalior, India.","DOI":"10.1109\/CICT56698.2022.9997996"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/10\/1748\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:35:58Z","timestamp":1760124958000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/10\/1748"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,16]]},"references-count":118,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["diagnostics13101748"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13101748","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,16]]}}}