Academic Journal
Cardio-Dense: Diagnosis of Cardiac Abnormalities Based on Phonocardiogram Using Improved Swin Transformer Through Lightweight Dense Blocks.
| Title: | Cardio-Dense: Diagnosis of Cardiac Abnormalities Based on Phonocardiogram Using Improved Swin Transformer Through Lightweight Dense Blocks. |
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| Authors: | Ahmed, Alaa E. S., Ibrahim, Mostafa E. A., Daadaa, Yassine |
| Source: | Diagnostics (2075-4418); May2026, Vol. 16 Issue 10, p1421, 27p |
| Subject Terms: | Heart valve diseases, Diagnosis, Transformer models, Cardiovascular diseases, Deep learning, Feature extraction, Signal processing, Heart sounds |
| Abstract: | 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' 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–frequency 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Diagnostics (2075-4418) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Biomedical Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edm&genre=article&issn=20754418&ISBN=&volume=16&issue=10&date=20260515&spage=1421&pages=1421-1447&title=Diagnostics (2075-4418)&atitle=Cardio-Dense%3A%20Diagnosis%20of%20Cardiac%20Abnormalities%20Based%20on%20Phonocardiogram%20Using%20Improved%20Swin%20Transformer%20Through%20Lightweight%20Dense%20Blocks.&aulast=Ahmed%2C%20Alaa%20E.%20S.&id=DOI:10.3390/diagnostics16101421 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Cardio-Dense: Diagnosis of Cardiac Abnormalities Based on Phonocardiogram Using Improved Swin Transformer Through Lightweight Dense Blocks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ahmed%2C+Alaa+E%2E+S%2E%22">Ahmed, Alaa E. S.</searchLink><br /><searchLink fieldCode="AR" term="%22Ibrahim%2C+Mostafa+E%2E+A%2E%22">Ibrahim, Mostafa E. A.</searchLink><br /><searchLink fieldCode="AR" term="%22Daadaa%2C+Yassine%22">Daadaa, Yassine</searchLink> – Name: TitleSource Label: Source Group: Src Data: Diagnostics (2075-4418); May2026, Vol. 16 Issue 10, p1421, 27p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Heart+valve+diseases%22">Heart valve diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Cardiovascular+diseases%22">Cardiovascular diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+sounds%22">Heart sounds</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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' 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–frequency 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Diagnostics (2075-4418) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/diagnostics16101421 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1421 Subjects: – SubjectFull: Heart valve diseases Type: general – SubjectFull: Diagnosis Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Cardiovascular diseases Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Heart sounds Type: general Titles: – TitleFull: Cardio-Dense: Diagnosis of Cardiac Abnormalities Based on Phonocardiogram Using Improved Swin Transformer Through Lightweight Dense Blocks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ahmed, Alaa E. S. – PersonEntity: Name: NameFull: Ibrahim, Mostafa E. A. – PersonEntity: Name: NameFull: Daadaa, Yassine IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20754418 Numbering: – Type: volume Value: 16 – Type: issue Value: 10 Titles: – TitleFull: Diagnostics (2075-4418) Type: main |
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