{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:14:57Z","timestamp":1778166897832,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2601).","award":["IMSIU-DDRSP2601"],"award-info":[{"award-number":["IMSIU-DDRSP2601"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Cardiovascular diseases (CVDs) are among the top sources of mortality worldwide. To properly diagnose cardiovascular diseases, a low-cost remedy based on phonocardiography (PCG) signals must be proposed. Several deep learning (DL)-driven CVD systems are now being developed to identify various phases of the disease. Nevertheless, the approaches\u2019 accuracy falls short of expectations, and they necessitate substantial processing resources and training data. Methods: This paper proposes Cardio-Dense, a hybrid framework for multi-class CVD detection from phonocardiogram signals. The PCG waveform is first denoised in the wavelet domain and then converted into a 2D time\u2013frequency spectrogram using continuous wavelet transform (CWT). We design a joint architecture that combines a Swin transformer for capturing global contextual dependencies with lightweight DenseBlocks for efficient local feature refinement, enabling robust learning from PCG spectrograms across five disease classes. Results: Experiments on PCG datasets achieve up to 0.977 accuracy, 0.975 sensitivity, 0.992 specificity, 0.978 F1-score, 0.978 AUC, and 0.976 precision, while maintaining low computational overhead suitable for real-time inference. Conclusions: The findings indicate that the proposed model provides an economical, non-invasive method for preliminary signal-level identification of multi-class heart valve diseases. It benefits clinicians by decreasing the need for arduous and error-prone manual PCG analysis. Furthermore, it offers quick, near-real-time categorization suitable for clinical and portable applications.<\/jats:p>","DOI":"10.3390\/diagnostics16101421","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T14:05:46Z","timestamp":1778162746000},"page":"1421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cardio-Dense: Diagnosis of Cardiac Abnormalities Based on Phonocardiogram Using Improved Swin Transformer Through Lightweight Dense Blocks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6692-4703","authenticated-orcid":false,"given":"Alaa E. S.","family":"Ahmed","sequence":"first","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0730-6857","authenticated-orcid":false,"given":"Mostafa E. A.","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3405-1554","authenticated-orcid":false,"given":"Yassine","family":"Daadaa","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021). A Report About Health, WHO. Available online: https:\/\/www.who.int\/health-topics\/cardiovascular-diseases#tab=tab_1."},{"key":"ref_2","first-page":"42696","article-title":"Cardiovascular Disease: A Review","volume":"51","author":"Soares","year":"2023","journal-title":"Biomed. J. Sci. Tech. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1109\/JBHI.2024.3357506","article-title":"Identification of congenital valvular murmurs in young patients using deep learning-based attention transformers and phonocardiograms","volume":"28","author":"Alkhodari","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5512243","DOI":"10.1155\/2021\/5512243","article-title":"Cardiac disorder classification by electrocardiogram sensing using deep neural network","volume":"2021","author":"Khan","year":"2021","journal-title":"Complexity"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Molcer, P.S., Kecskes, I., Deli\u0107, V., Domijan, E., and Domijan, M. (2010). Examination of formant frequencies for further classification of heart murmurs. IEEE 8th International Symposium on Intelligent Systems and Informatics, IEEE.","DOI":"10.1109\/SISY.2010.5647147"},{"key":"ref_6","first-page":"806","article-title":"Systematic Review for Phonocardiography Classification Based on Machine Learning","volume":"14","author":"Altaf","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108094","DOI":"10.1016\/j.compeleceng.2022.108094","article-title":"Artificial neural network-based cardiovascular disease prediction using spectral features","volume":"101","author":"Khan","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102257","DOI":"10.1016\/j.artmed.2022.102257","article-title":"Automatic pediatric congenital heart disease classification based on heart sound signal","volume":"126","author":"Xu","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103730","DOI":"10.1016\/j.bspc.2022.103730","article-title":"Development and assessment of machine learning based heart disease detection using imbalanced heart sound signal","volume":"76","author":"Rath","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101560","DOI":"10.1016\/j.bspc.2019.101560","article-title":"Abnormal heart sound detection using temporal quasi-periodic features and long short-term memory without segmentation","volume":"53","author":"Zhang","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107242","DOI":"10.1016\/j.apacoust.2020.107242","article-title":"An improved method to detect coronary artery disease using phonocardiogram signals in noisy environment","volume":"164","author":"Pathak","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Asmare, M.H., Filtjens, B., Woldehanna, F., Janssens, L., and Vanrumste, B. (2021). Rheumatic heart disease screening based on phonocardiogram. Sensors, 21.","DOI":"10.3390\/s21196558"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ahmed, A.E., Abbas, Q., Daadaa, Y., Qureshi, I., Perumal, G., and Ibrahim, M.E. (2023). A residual-dense-based convolutional neural network architecture for recognition of cardiac health based on ECG signals. Sensors, 23.","DOI":"10.3390\/s23167204"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e13535","DOI":"10.1111\/exsy.13535","article-title":"Artificial intelligence for heart sound classification: A review","volume":"41","author":"Chen","year":"2024","journal-title":"Expert Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ibrahim, N., Jamal, N., Sha\u2019abani, M.N.A.H., and Mahadi, L.F. (2021). A comparative study of heart sound signal classification based on temporal, spectral and geometric features. 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), IEEE.","DOI":"10.1109\/IECBES48179.2021.9398810"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Singh, S.A., Majumder, S., and Mishra, M. (2019). Classification of short unsegmented heart sound based on deep learning. 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE.","DOI":"10.1109\/I2MTC.2019.8826991"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106449","DOI":"10.1016\/j.asoc.2020.106449","article-title":"Autonomous learning multiple-model zero-order classifier for heart sound classification","volume":"94","author":"Soares","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_18","first-page":"382","article-title":"Analysis of training optimization algorithms in the NARX neural network for classification of heart sound signals","volume":"13","author":"Khaled","year":"2022","journal-title":"Int. J. Sci. Eng. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"055006","DOI":"10.1088\/1361-6579\/ab8770","article-title":"Automatic heart sound classification from segmented\/unsegmented phonocardiogram signals using time and frequency features","volume":"41","author":"Khan","year":"2020","journal-title":"Physiol. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cheng, X., Wang, P., and She, C. (2020). Biometric identification method for heart sound based on multimodal multiscale dispersion entropy. Entropy, 22.","DOI":"10.3390\/e22020238"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e13411","DOI":"10.1111\/exsy.13411","article-title":"Machine learning-based classification of multiple heart disorders from PCG signals","volume":"40","author":"Talal","year":"2023","journal-title":"Expert Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Son, G.Y., and Kwon, S. (2018). Classification of heart sound signal using multiple features. Appl. Sci., 8.","DOI":"10.3390\/app8122344"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1038\/s41598-024-53778-7","article-title":"Artificial intelligence framework for heart disease classification from audio signals","volume":"14","author":"Abbas","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106727","DOI":"10.1016\/j.cmpb.2022.106727","article-title":"Cardi-Net: A deep neural network for classification of cardiac disease using phonocardiogram signal","volume":"219","author":"Khan","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107406","DOI":"10.1016\/j.engappai.2023.107406","article-title":"Elastic net regularization and gabor dictionary for classification of heart sound signals using deep learning","volume":"127","author":"Fakhry","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"36955","DOI":"10.1109\/ACCESS.2021.3063129","article-title":"CardioXNet: A novel lightweight deep learning framework for cardiovascular disease classification using heart sound recordings","volume":"9","author":"Shuvo","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Abbas, Q., Hussain, A., and Baig, A.R. (2022). Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers. Diagnostics, 12.","DOI":"10.3390\/diagnostics12123109"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2300549","DOI":"10.1002\/adts.202300549","article-title":"Heart Sounds Classification Based on High-Order Spectrogram and Multi-Convolutional Neural Network after a New Screening Strategy","volume":"7","author":"Li","year":"2024","journal-title":"Adv. Theory Simul."},{"key":"ref_29","first-page":"1","article-title":"FHSU-NETR: Transformer-Based Deep Learning Model for the Detection of Fetal Heart Sounds in Phonocardiography","volume":"Volume 50","author":"Almadani","year":"2023","journal-title":"2023 Computing in Cardiology (CinC)"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"095003","DOI":"10.1088\/1361-6579\/ac1d59","article-title":"Deep learning based classification of unsegmented phonocardiogram spectrograms leveraging transfer learning","volume":"42","author":"Khan","year":"2021","journal-title":"Physiol. Meas."},{"key":"ref_31","unstructured":"Sun, Y., Liu, Y., Chen, Y., Wang, Y., and Chen, H. (2023). PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mastropietro, A., Casali, N., Taccogna, M.G., D\u2019Angelo, M.G., Rizzo, G., and Peruzzo, D. (2024). Classification of Muscular Dystrophies from MR Images Improves Using the Swin Transformer Deep Learning Model. Bioengineering, 11.","DOI":"10.3390\/bioengineering11060580"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sarker, S., Sarker, P., Bebis, G., and Tavakkoli, A. (2024). MV-Swin-T: Mammogram Classification with Multi-View Swin Transformer. arXiv.","DOI":"10.1109\/ISBI56570.2024.10635578"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cheng, J., and Sun, K. (2023). Heart Sound Classification Network Based on Convolution and Transformer. Sensors, 23.","DOI":"10.3390\/s23198168"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Baima, N., Wang, T., Zhao, C.-K., Chen, S., Zhao, C., and Lei, B. (2023). Dense Swin Transformer for Classification of Thyroid Nodule. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC40787.2023.10340827"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","article-title":"Ideal spatial adaptation by wavelet shrinkage","volume":"81","author":"Donoho","year":"1994","journal-title":"Biometrika"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105604","DOI":"10.1016\/j.cmpb.2020.105604","article-title":"Classification of heart sound signals using a novel deep WaveNet model","volume":"196","author":"Oh","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_38","first-page":"312","article-title":"Enhancing Cardiac HealthDiagnoses Through Machine Learning Analysis of Phonocardiograms (PCG)","volume":"3","author":"Popalzai","year":"2025","journal-title":"J. Data Sci. Intell. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhu, B., Zhou, Z., Yu, S., Liang, X., Xie, Y., and Sun, Q. (2024). Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet\/CinC Challenge 2016 Database. Electronics, 13.","DOI":"10.3390\/electronics13163222"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8316","DOI":"10.1109\/ACCESS.2018.2889437","article-title":"Algorithms for automatic analysis and classification of heart sounds\u2014A systematic review","volume":"7","author":"Dwivedi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_41","unstructured":"Liu, C., Springer, D., Moody, B., Silva, I., Johnson, A., Samieinasab, M., Sameni, R., Mark, R., and Clifford, G.D. (2016). Classification of Heart Sound Recordings: The Physionet Computing in Cardiology Challenge 2016, PhysioNet."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/10\/1421\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T14:33:48Z","timestamp":1778164428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/10\/1421"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,7]]},"references-count":41,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["diagnostics16101421"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16101421","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,7]]}}}