Congenital Heart Disease recognition using Deep Learning/Transformer models
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915286297346048 |
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| author | Amangeldi, Aidar Yarovenko, Vladislav Taigonyrov, Angsar |
| author_facet | Amangeldi, Aidar Yarovenko, Vladislav Taigonyrov, Angsar |
| contents | Congenital Heart Disease (CHD) remains a leading cause of infant morbidity and mortality, yet non-invasive screening methods often yield false negatives. Deep learning models, with their ability to automatically extract features, can assist doctors in detecting CHD more effectively. In this work, we investigate the use of dual-modality (sound and image) deep learning methods for CHD diagnosis. We achieve 73.9% accuracy on the ZCHSound dataset and 80.72% accuracy on the DICOM Chest X-ray dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08242 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Congenital Heart Disease recognition using Deep Learning/Transformer models Amangeldi, Aidar Yarovenko, Vladislav Taigonyrov, Angsar Computer Vision and Pattern Recognition Congenital Heart Disease (CHD) remains a leading cause of infant morbidity and mortality, yet non-invasive screening methods often yield false negatives. Deep learning models, with their ability to automatically extract features, can assist doctors in detecting CHD more effectively. In this work, we investigate the use of dual-modality (sound and image) deep learning methods for CHD diagnosis. We achieve 73.9% accuracy on the ZCHSound dataset and 80.72% accuracy on the DICOM Chest X-ray dataset. |
| title | Congenital Heart Disease recognition using Deep Learning/Transformer models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.08242 |