A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System Measurements
Microwave signal-based binary classification for detecting the presence of stroke presents a promising avenue for cost-effective and portable diagnosis. However, implementing this technology in real-world settings remains challenging due to difficulties in accurately modeling wave scattering, which...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10907859/ |
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| author | Wei Yuan Phawis Thammasorn Lingxiao Wang Shih Mo |
| author_facet | Wei Yuan Phawis Thammasorn Lingxiao Wang Shih Mo |
| author_sort | Wei Yuan |
| collection | DOAJ |
| description | Microwave signal-based binary classification for detecting the presence of stroke presents a promising avenue for cost-effective and portable diagnosis. However, implementing this technology in real-world settings remains challenging due to difficulties in accurately modeling wave scattering, which often leads to suboptimal performance of classification systems. Our proposed deep learning framework addresses this issue by bypassing complex modeling tasks and directly extracting stroke signatures from wavelet-filtered microwave signals, combined with clinical variables and antenna measurements. Experiments conducted with total of 431 real-world stroke patients demonstrate that our system significantly outperforms recent classification approaches achieving 76.19% accuracy. Furthermore, our approach shows comparable performance to magnetic resonance imaging (MRI) based deep learning classification, in scenarios with limited imaging data. These results not only highlight the potential of microwave imaging (MWI) as a critical diagnostic tool but also suggest that MWI devices could become a viable option for clinical stroke diagnosis in the near future. |
| format | Article |
| id | doaj-art-49d475d22e304cb28e43445ff4300995 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-49d475d22e304cb28e43445ff43009952025-08-20T03:08:35ZengIEEEIEEE Access2169-35362025-01-0113399353994910.1109/ACCESS.2025.354655810907859A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System MeasurementsWei Yuan0Phawis Thammasorn1https://orcid.org/0000-0002-1592-9766Lingxiao Wang2Shih Mo3Chengdu Pidu District People’s Hospital, Chengdu, ChinaTiposi, Milpitas, CA, USATiposi, Milpitas, CA, USATiposi, Milpitas, CA, USAMicrowave signal-based binary classification for detecting the presence of stroke presents a promising avenue for cost-effective and portable diagnosis. However, implementing this technology in real-world settings remains challenging due to difficulties in accurately modeling wave scattering, which often leads to suboptimal performance of classification systems. Our proposed deep learning framework addresses this issue by bypassing complex modeling tasks and directly extracting stroke signatures from wavelet-filtered microwave signals, combined with clinical variables and antenna measurements. Experiments conducted with total of 431 real-world stroke patients demonstrate that our system significantly outperforms recent classification approaches achieving 76.19% accuracy. Furthermore, our approach shows comparable performance to magnetic resonance imaging (MRI) based deep learning classification, in scenarios with limited imaging data. These results not only highlight the potential of microwave imaging (MWI) as a critical diagnostic tool but also suggest that MWI devices could become a viable option for clinical stroke diagnosis in the near future.https://ieeexplore.ieee.org/document/10907859/Deep learningmicrowave signal processingstroke classificationhealth caremedical image analysis |
| spellingShingle | Wei Yuan Phawis Thammasorn Lingxiao Wang Shih Mo A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System Measurements IEEE Access Deep learning microwave signal processing stroke classification health care medical image analysis |
| title | A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System Measurements |
| title_full | A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System Measurements |
| title_fullStr | A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System Measurements |
| title_full_unstemmed | A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System Measurements |
| title_short | A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining Signal Analysis, Clinical Variables, and Antenna System Measurements |
| title_sort | comprehensive deep learning framework for microwave stroke classification combining signal analysis clinical variables and antenna system measurements |
| topic | Deep learning microwave signal processing stroke classification health care medical image analysis |
| url | https://ieeexplore.ieee.org/document/10907859/ |
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