{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:31:12Z","timestamp":1772555472523,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100018778","name":"Al-Azhar University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100018778","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper proposed a novel approach for detecting lung sound disorders using deep learning feature fusion. The lung sound dataset are oversampled and converted into spectrogram images. Then, extracting deep features from CNN architectures, which are pre-trained on large-scale image datasets. These deep features capture rich representations of spectrogram images from the input signals, allowing for a comprehensive analysis of lung disorders. Next, a fusion technique is employed to combine the extracted features from multiple CNN architectures totlaly 8064 feature. This fusion process enhances the discriminative power of the features, facilitating more accurate and robust detection of lung disorders. To further improve the detection performance, an improved CNN Architecture is employed. To evaluate the effectiveness of the proposed approach, an experiments conducted on a large dataset of lung disorder signals. The results demonstrate that the deep feature fusion from different CNN architectures, combined with different CNN Layers, achieves superior performance in lung disorder detection. Compared to individual CNN architectures, the proposed approach achieves higher accuracy, sensitivity, and specificity, effectively reducing false negatives and false positives. The proposed model achieves 96.03% accuracy, 96.53% Sensitivity, 99.424% specificity, 96.52% precision, and 96.50% F1 Score when predicting lung diseases from sound files. This approach has the potential to assist healthcare professionals in the early detection and diagnosis of lung disorders, ultimately leading to improved patient outcomes and enhanced healthcare practices.<\/jats:p>","DOI":"10.1007\/s00500-024-09866-x","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T15:07:22Z","timestamp":1721747242000},"page":"11667-11683","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Deep learning and feature fusion-based lung sound recognition model to diagnoses the respiratory diseases"],"prefix":"10.1007","volume":"28","author":[{"given":"Sara A.","family":"Shehab","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3907-8588","authenticated-orcid":false,"given":"Kamel K.","family":"Mohammed","sequence":"additional","affiliation":[]},{"given":"Ashraf","family":"Darwish","sequence":"additional","affiliation":[]},{"given":"Aboul Ella","family":"Hassanien","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"9866_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101788","volume":"57","author":"Z Abduh","year":"2020","unstructured":"Abduh Z, Nehary EA, Abdel Wahed M, Kadah Y (2020) Classification of heart sounds using fractional Fourier transform based Mel-frequency spectral coefficients and traditional classifiers. Biomed Signal Process Control 57:101788. https:\/\/doi.org\/10.1016\/j.bspc.2019.101788","journal-title":"Biomed Signal Process Control"},{"key":"9866_CR2","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.bspc.2018.05.014","volume":"45","author":"G Altan","year":"2018","unstructured":"Altan G, Kutlu Y, Pekmezci AO, Nural S (2018) Deep learning with 3D-second order difference plot on respiratory sounds. Biomed Signal Process Control 45:58\u201369. https:\/\/doi.org\/10.1016\/j.bspc.2018.05.014","journal-title":"Biomed Signal Process Control"},{"issue":"5","key":"9866_CR3","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/JBHI.2019.2931395","volume":"24","author":"G Altan","year":"2020","unstructured":"Altan G, Kutlu Y, Allahverdi N (2020a) Deep learning on computerized analysis of chronic obstructive pulmonary disease. IEEE J Biomed Health Inform 24(5):1344\u20131350. https:\/\/doi.org\/10.1109\/JBHI.2019.2931395","journal-title":"IEEE J Biomed Health Inform"},{"key":"9866_CR4","doi-asserted-by":"publisher","first-page":"2979","DOI":"10.3906\/ELK-2004-68","volume":"28","author":"G Altan","year":"2020","unstructured":"Altan G, Kutlu Y, Gok\u00e7en A (2020b) Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds. Turkish J Electr Eng Comput Sci 28:2979\u20132996. https:\/\/doi.org\/10.3906\/ELK-2004-68","journal-title":"Turkish J Electr Eng Comput Sci"},{"key":"9866_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107367","volume":"94","author":"N Asatani","year":"2021","unstructured":"Asatani N, Kamiya T, Mabu S, Kido S (2021) Classification of respiratory sounds using improved convolutional recurrent neural network. Comput Electr Eng 94:107367. https:\/\/doi.org\/10.1016\/j.compeleceng.2021.107367","journal-title":"Comput Electr Eng"},{"key":"9866_CR6","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1186\/s13640-017-0213-2","volume":"2017","author":"M Aykanat","year":"2017","unstructured":"Aykanat M, K\u0131l\u0131\u00e7 \u00d6, Kurt B et al (2017) Classification of lung sounds using convolutional neural networks. J Image Video Proc 2017:65. https:\/\/doi.org\/10.1186\/s13640-017-0213-2","journal-title":"J Image Video Proc"},{"issue":"6","key":"9866_CR7","doi-asserted-by":"publisher","first-page":"333","DOI":"10.21037\/atm-22-534","volume":"10","author":"Y Cao","year":"2022","unstructured":"Cao Y, Zhang C, Peng C, Zhang G, Sun Y, Jiang X, Wang Z, Zhang D, Wang L, Liu J (2022) A convolutional neural network-based COVID-19 detection method using chest CT images. Ann Transl Med 10(6):333. https:\/\/doi.org\/10.21037\/atm-22-534","journal-title":"Ann Transl Med"},{"issue":"2","key":"9866_CR8","doi-asserted-by":"publisher","first-page":"153","DOI":"10.12720\/jait.12.2.153-158","volume":"12","author":"Y Chen","year":"2021","unstructured":"Chen Y, Du W, Duan X, Ma Y, Zhang H (2021) Squeeze-and-excitation convolutional neural network for classification of malignant and benign lung nodules. J Adv Inf Technol 12(2):153\u2013158. https:\/\/doi.org\/10.12720\/jait.12.2.153-158","journal-title":"J Adv Inf Technol"},{"key":"9866_CR9","doi-asserted-by":"publisher","first-page":"53027","DOI":"10.1109\/access.2022.3174678","volume":"10","author":"Y Choi","year":"2022","unstructured":"Choi Y, Choi H, Lee H, Lee S, Lee H (2022) Lightweight skip connections with efficient feature stacking for respiratory sound classification. IEEE Access 10:53027\u201353042. https:\/\/doi.org\/10.1109\/access.2022.3174678","journal-title":"IEEE Access"},{"key":"9866_CR10","doi-asserted-by":"crossref","unstructured":"D\u2019Angelo G, Farsimadan E, Palmieri F (2023) Recurrence plots-based network attack classification using CNN-autoencoders. In: Gervasi O et al (eds) Computational science and its applications\u2014ICCSA 2023 workshops. ICCSA 2023. Lecture notes in computer science, vol 14105. Springer, Cham","DOI":"10.1007\/978-3-031-37108-0_13"},{"key":"9866_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103905","volume":"78","author":"JA Dar","year":"2022","unstructured":"Dar JA, Srivastava KK, Lone SA (2022) Spectral features and optimal hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals. Biomed Signal Process Control 78:103905. https:\/\/doi.org\/10.1016\/j.bspc.2022.103905","journal-title":"Biomed Signal Process Control"},{"key":"9866_CR12","doi-asserted-by":"publisher","first-page":"4759","DOI":"10.1007\/s12652-021-03184-y","volume":"13","author":"M Fraiwan","year":"2022","unstructured":"Fraiwan M, Fraiwan L, Alkhodari M et al (2022) Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory. J Ambient Intell Human Comput 13:4759\u20135477. https:\/\/doi.org\/10.1007\/s12652-021-03184-y","journal-title":"J Ambient Intell Human Comput"},{"key":"9866_CR13","doi-asserted-by":"publisher","first-page":"10934","DOI":"10.1109\/ACCESS.2022.3144355","volume":"10","author":"E Grooby","year":"2022","unstructured":"Grooby E et al (2022) Real-time multi-level neonatal heart and lung sound quality assessment for telehealth applications. IEEE Access 10:10934\u201310948. https:\/\/doi.org\/10.1109\/ACCESS.2022.3144355","journal-title":"IEEE Access"},{"key":"9866_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102947","volume":"70","author":"S Gupta","year":"2021","unstructured":"Gupta S, Agrawal M, Deepak D (2021) Gammatonegram based triple classification of lung sounds using deep convolutional neural network with transfer learning. Biomed Signal Process Control 70:102947. https:\/\/doi.org\/10.1016\/j.bspc.2021.102947","journal-title":"Biomed Signal Process Control"},{"issue":"21","key":"9866_CR15","doi-asserted-by":"publisher","first-page":"30615","DOI":"10.1007\/s11042-022-12156-z","volume":"81","author":"A Haghanifar","year":"2022","unstructured":"Haghanifar A, Majdabadi MM, Choi Y, Deivalakshmi S, Ko S (2022) COVID-CXNet: detecting COVID-19 in frontal chest X-ray images using deep learning. Multimed Tools Appl 81(21):30615\u201330645. https:\/\/doi.org\/10.1007\/s11042-022-12156-z","journal-title":"Multimed Tools Appl"},{"key":"9866_CR16","doi-asserted-by":"publisher","first-page":"17186","DOI":"10.1038\/s41598-021-96724-7","volume":"11","author":"Y Kim","year":"2021","unstructured":"Kim Y, Hyon Y, Jung SS et al (2021) Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci Rep 11:17186. https:\/\/doi.org\/10.1038\/s41598-021-96724-7","journal-title":"Sci Rep"},{"key":"9866_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107398","volume":"151","author":"S Kiranyaz","year":"2021","unstructured":"Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman DJ (2021) 1D convolutional neural networks and applications: a survey. Mech Syst Signal Process 151:107398. https:\/\/doi.org\/10.1016\/j.ymssp.2020.107398","journal-title":"Mech Syst Signal Process"},{"key":"9866_CR18","doi-asserted-by":"crossref","unstructured":"Koike T, Qian K, Kong Q, Plumbley MD, Schuller BW, Yamamoto Y (2020) Audio for audio is better? An investigation on transfer learning models for heart sound classification. In: Annual international conference of the IEEE engineering in medicine & biology society, Montreal, QC, Canada, pp 74\u201377","DOI":"10.1109\/EMBC44109.2020.9175450"},{"issue":"18\u201320","key":"9866_CR19","doi-asserted-by":"publisher","first-page":"3329","DOI":"10.1140\/epjs\/s11734-022-00432-w","volume":"231","author":"L Kranthi Kumar","year":"2022","unstructured":"Kranthi Kumar L, Alphonse PJA (2022) COVID-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds. Eur Phys J Spec Top 231(18\u201320):3329\u20133346. https:\/\/doi.org\/10.1140\/epjs\/s11734-022-00432-w","journal-title":"Eur Phys J Spec Top"},{"key":"9866_CR20","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.procs.2022.12.412","volume":"218","author":"A Lembhe","year":"2023","unstructured":"Lembhe A, Motarwar P, Patil R, Elias S (2023) Enhancement in skin cancer detection using image super resolution and convolutional neural network. Procedia Comput Sci 218:164\u2013173. https:\/\/doi.org\/10.1016\/j.procs.2022.12.412","journal-title":"Procedia Comput Sci"},{"key":"9866_CR21","doi-asserted-by":"publisher","first-page":"2002108","DOI":"10.1183\/13993003.02108-2020","volume":"56","author":"JM Leung","year":"2020","unstructured":"Leung JM, Niikura M, Yang CWT, Sin DD (2020) COVID-19 and COPD. Eur Respir J 56:2002108","journal-title":"Eur Respir J"},{"key":"9866_CR22","doi-asserted-by":"publisher","unstructured":"Ma Y, Xu X, Li Y (2020) LungRN+NL: an improved adventitious lung sound classification using non-local block ResNet neural network with mixup data augmentation. In: Interspeech. https:\/\/doi.org\/10.21437\/interspeech.2020-2487","DOI":"10.21437\/interspeech.2020-2487"},{"key":"9866_CR23","doi-asserted-by":"publisher","DOI":"10.1080\/08839514.2022.2033473","author":"DO Oyewola","year":"2022","unstructured":"Oyewola DO et al (2022) A novel data augmentation convolutional neural network for detecting malaria parasite in blood smear images. Appl Artif Intell. https:\/\/doi.org\/10.1080\/08839514.2022.2033473","journal-title":"Appl Artif Intell"},{"issue":"21","key":"9866_CR24","doi-asserted-by":"publisher","first-page":"10715","DOI":"10.3390\/app122110715","volume":"12","author":"K Park","year":"2022","unstructured":"Park K, Choi Y, Lee H (2022) COVID-19 CXR classification: applying domain extension transfer learning and deep learning. Appl Sci 12(21):10715. https:\/\/doi.org\/10.3390\/app122110715","journal-title":"Appl Sci"},{"issue":"3","key":"9866_CR25","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.3390\/s22031232","volume":"22","author":"G Petmezas","year":"2022","unstructured":"Petmezas G, Cheimariotis G-A, Stefanopoulos L, Rocha B, Paiva RP, Katsaggelos AK, Maglaveras N (2022) Automated lung sound classification using a hybrid CNN-LSTM network and focal loss function. Sensors 22(3):1232. https:\/\/doi.org\/10.3390\/s22031232","journal-title":"Sensors"},{"key":"9866_CR26","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1080\/03091902.2022.2040624","volume":"46","author":"H Pham Thi Viet","year":"2022","unstructured":"Pham Thi Viet H, Nguyen Thi Ngoc H, Tran Anh V, Hoang Quang H (2022) Classification of lung sounds using scalogram representation of sound segments and convolutional neural network. J Med Eng Technol 46:270\u2013279. https:\/\/doi.org\/10.1080\/03091902.2022.2040624","journal-title":"J Med Eng Technol"},{"issue":"2","key":"9866_CR27","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1109\/JSTSP.2022.3142514","volume":"16","author":"A Ponomarchuk","year":"2022","unstructured":"Ponomarchuk A, Burenko I, Malkin E, Nazarov I, Kokh V, Avetisian M, Zhukov L (2022) Project achoo: a practical model and application for COVID-19 detection from recordings of breath, voice, and cough. IEEE J Sel Top Signal Process 16(2):175\u2013187. https:\/\/doi.org\/10.1109\/JSTSP.2022.3142514","journal-title":"IEEE J Sel Top Signal Process"},{"key":"9866_CR28","doi-asserted-by":"publisher","first-page":"3443","DOI":"10.1038\/s41598-024-53792-9","volume":"14","author":"MS Priyadarshini","year":"2024","unstructured":"Priyadarshini MS, Bajaj M, Prokop L et al (2024) Perception of power quality disturbances using Fourier, short-time Fourier, continuous and discrete wavelet transforms. Sci Rep 14:3443. https:\/\/doi.org\/10.1038\/s41598-024-53792-9","journal-title":"Sci Rep"},{"key":"9866_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04154-5","author":"A Qayyum","year":"2021","unstructured":"Qayyum A, Razzak I, Tanveer M, Kumar A (2021) Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis. Ann Oper Res. https:\/\/doi.org\/10.1007\/s10479-021-04154-5","journal-title":"Ann Oper Res"},{"key":"9866_CR30","doi-asserted-by":"crossref","unstructured":"Rocha BM, Filos D, Mendes L, Vogiatzis I, Perantoni E, Kaimakamis E, Natsiavas P, Oliveira A, Jacome C, Marques A, Paiva RP, Chouvarda I, Carvalho P, Maglaveras N (2017) A respiratory sound database for the development of automated classification. In: International conference on biomedical and health informatics. Springer, Singapore","DOI":"10.1007\/978-981-10-7419-6_6"},{"issue":"3","key":"9866_CR31","first-page":"18","volume":"4","author":"FM Salman","year":"2020","unstructured":"Salman FM, Abu-Naser SS, Alajrami E, Abu-Nasser BS, Alashqar BA (2020) Covid-19 detection using artificial intelligence. Int J Acad Eng Res 4(3):18\u201325","journal-title":"Int J Acad Eng Res"},{"issue":"3","key":"9866_CR32","doi-asserted-by":"publisher","first-page":"158","DOI":"10.4103\/1817-1737.160831","volume":"10","author":"M Sarkar","year":"2015","unstructured":"Sarkar M, Madabhavi I, Niranjan N, Dogra M (2015) Auscultation of the respiratory system. Ann Thorac Med 10(3):158\u2013168. https:\/\/doi.org\/10.4103\/1817-1737.160831","journal-title":"Ann Thorac Med"},{"key":"9866_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119772","volume":"221","author":"S Sharma","year":"2023","unstructured":"Sharma S, Singh S (2023) ISL recognition system using integrated mobile-net and transfer learning method. Expert Syst Appl 221:119772. https:\/\/doi.org\/10.1016\/j.eswa.2023.119772","journal-title":"Expert Syst Appl"},{"key":"9866_CR34","doi-asserted-by":"publisher","first-page":"139438","DOI":"10.1109\/ACCESS.2019.2943492","volume":"7","author":"L Shi","year":"2019","unstructured":"Shi L, Du K, Zhang C, Ma H, Yan W (2019) Lung sound recognition algorithm based on VGGish-BiGRU. IEEE Access 7:139438\u2013139449. https:\/\/doi.org\/10.1109\/ACCESS.2019.2943492","journal-title":"IEEE Access"},{"issue":"7","key":"9866_CR35","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1109\/JBHI.2020.3048006","volume":"25","author":"SB Shuvo","year":"2021","unstructured":"Shuvo SB, Ali SN, Swapnil SI, Hasan T, Bhuiyan MIH (2021) A lightweight CNN model for detecting respiratory diseases from lung auscultation sounds using EMD-CWT-based hybrid scalogram. IEEE J Biomed Health Inform 25(7):2595\u20132603. https:\/\/doi.org\/10.1109\/JBHI.2020.3048006","journal-title":"IEEE J Biomed Health Inform"},{"key":"9866_CR36","doi-asserted-by":"publisher","first-page":"4180949","DOI":"10.1155\/2019\/4180949","volume":"2019","author":"O Stephen","year":"2019","unstructured":"Stephen O, Sain M, Maduh UJ, Jeong D-U (2019) An efficient deep learning approach to pneumonia classification in healthcare. J Healthc Eng 2019:4180949. https:\/\/doi.org\/10.1155\/2019\/4180949","journal-title":"J Healthc Eng"},{"key":"9866_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2023.100278","volume":"8","author":"V Venugopal","year":"2023","unstructured":"Venugopal V, Raj NI, Nath MK, Stephen N (2023) A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images. Decis Anal J 8:100278. https:\/\/doi.org\/10.1016\/j.dajour.2023.100278","journal-title":"Decis Anal J"},{"key":"9866_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101309","volume":"79","author":"C Wang","year":"2023","unstructured":"Wang C, Wang Z, Zhang S, Tan J (2023) Adam-assisted quantum particle swarm optimization guided by length of potential well for numerical function optimization. Swarm Evol Comput 79:101309. https:\/\/doi.org\/10.1016\/j.swevo.2023.101309","journal-title":"Swarm Evol Comput"},{"key":"9866_CR39","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.patcog.2019.03.005","volume":"92","author":"W Hao","year":"2019","unstructured":"Hao W, Zhang Z (2019) Spatiotemporal distilled dense-connectivity network for video action recognition. Pattern Recogn 92:13\u201324. https:\/\/doi.org\/10.1016\/j.patcog.2019.03.005","journal-title":"Pattern Recogn"},{"key":"9866_CR40","unstructured":"World Health Organization (2023) World Health Statistics: monitoring health for the SDGs, sustainable development goals. Available at https:\/\/www.who.int\/publications\/i\/item\/9789240074323"},{"key":"9866_CR41","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1177\/15353702221115428","volume":"247","author":"T Xia","year":"2022","unstructured":"Xia T, Han J, Mascolo C (2022) Exploring machine learning for audio-based respiratory condition screening: a concise review of databases, methods, and open issues. Exp Biol Med 247:2053\u20132061. https:\/\/doi.org\/10.1177\/15353702221115428","journal-title":"Exp Biol Med"},{"key":"9866_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2023.102490","volume":"79","author":"R Yang","year":"2023","unstructured":"Yang R, Cui X, Qin Q, Deng Z, Lan R, Luo X (2023) Fast RF-UIC: a fast unsupervised image captioning model. Displays 79:102490. https:\/\/doi.org\/10.1016\/j.displa.2023.102490","journal-title":"Displays"},{"key":"9866_CR43","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s40537-023-00857-7","volume":"11","author":"C Yang","year":"2024","unstructured":"Yang C, Fridgeirsson EA, Kors JA et al (2024) Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data. J Big Data 11:7. https:\/\/doi.org\/10.1186\/s40537-023-00857-7","journal-title":"J Big Data"},{"key":"9866_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2021.108258","volume":"182","author":"T Zhang","year":"2021","unstructured":"Zhang T, Feng G, Liang J, An T (2021) Acoustic scene classification based on Mel spectrogram decomposition and model merging. Appl Acoust 182:108258. https:\/\/doi.org\/10.1016\/j.apacoust.2021.108258","journal-title":"Appl Acoust"},{"key":"9866_CR45","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.714811","volume":"8","author":"R Zulfiqar","year":"2021","unstructured":"Zulfiqar R, Majeed F, Irfan R, Rauf HT, Benkhelifa E, Belkacem AN (2021) Abnormal respiratory sounds classification using deep CNN through artificial noise addition. Front Med 8:714811. https:\/\/doi.org\/10.3389\/fmed.2021.714811","journal-title":"Front Med"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-024-09866-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-024-09866-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-024-09866-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T07:06:24Z","timestamp":1729321584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-024-09866-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,23]]},"references-count":45,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["9866"],"URL":"https:\/\/doi.org\/10.1007\/s00500-024-09866-x","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,23]]},"assertion":[{"value":"23 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}