TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices
Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Elhanashi, Abdussalam [verfasserIn] Dini, Pierpaolo [verfasserIn] Saponara, Sergio [verfasserIn] Zheng, Qinghe [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of real-time image processing - Springer Berlin Heidelberg, 2006, 21(2024), 4 vom: 26. Juni |
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| Übergeordnetes Werk: |
volume:21 ; year:2024 ; number:4 ; day:26 ; month:06 |
| Links: |
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| DOI / URN: |
10.1007/s11554-024-01500-1 |
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| Katalog-ID: |
SPR056370822 |
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| 520 | |a Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field. | ||
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| 700 | 1 | |a Saponara, Sergio |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Qinghe |e verfasserin |4 aut | |
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10.1007/s11554-024-01500-1 doi (DE-627)SPR056370822 (SPR)s11554-024-01500-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Elhanashi, Abdussalam verfasserin aut TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field. Stroke detection (dpeaa)DE-He213 Federated learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Real time (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 NVIDIA platforms (dpeaa)DE-He213 Dini, Pierpaolo verfasserin aut Saponara, Sergio verfasserin aut Zheng, Qinghe verfasserin aut Enthalten in Journal of real-time image processing Springer Berlin Heidelberg, 2006 21(2024), 4 vom: 26. Juni (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:21 year:2024 number:4 day:26 month:06 https://dx.doi.org/10.1007/s11554-024-01500-1 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 4 26 06 |
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10.1007/s11554-024-01500-1 doi (DE-627)SPR056370822 (SPR)s11554-024-01500-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Elhanashi, Abdussalam verfasserin aut TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field. Stroke detection (dpeaa)DE-He213 Federated learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Real time (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 NVIDIA platforms (dpeaa)DE-He213 Dini, Pierpaolo verfasserin aut Saponara, Sergio verfasserin aut Zheng, Qinghe verfasserin aut Enthalten in Journal of real-time image processing Springer Berlin Heidelberg, 2006 21(2024), 4 vom: 26. Juni (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:21 year:2024 number:4 day:26 month:06 https://dx.doi.org/10.1007/s11554-024-01500-1 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 4 26 06 |
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10.1007/s11554-024-01500-1 doi (DE-627)SPR056370822 (SPR)s11554-024-01500-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Elhanashi, Abdussalam verfasserin aut TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field. Stroke detection (dpeaa)DE-He213 Federated learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Real time (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 NVIDIA platforms (dpeaa)DE-He213 Dini, Pierpaolo verfasserin aut Saponara, Sergio verfasserin aut Zheng, Qinghe verfasserin aut Enthalten in Journal of real-time image processing Springer Berlin Heidelberg, 2006 21(2024), 4 vom: 26. Juni (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:21 year:2024 number:4 day:26 month:06 https://dx.doi.org/10.1007/s11554-024-01500-1 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 4 26 06 |
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10.1007/s11554-024-01500-1 doi (DE-627)SPR056370822 (SPR)s11554-024-01500-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Elhanashi, Abdussalam verfasserin aut TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field. Stroke detection (dpeaa)DE-He213 Federated learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Real time (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 NVIDIA platforms (dpeaa)DE-He213 Dini, Pierpaolo verfasserin aut Saponara, Sergio verfasserin aut Zheng, Qinghe verfasserin aut Enthalten in Journal of real-time image processing Springer Berlin Heidelberg, 2006 21(2024), 4 vom: 26. Juni (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:21 year:2024 number:4 day:26 month:06 https://dx.doi.org/10.1007/s11554-024-01500-1 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 4 26 06 |
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10.1007/s11554-024-01500-1 doi (DE-627)SPR056370822 (SPR)s11554-024-01500-1-e DE-627 ger DE-627 rakwb eng 620 004 VZ Elhanashi, Abdussalam verfasserin aut TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field. Stroke detection (dpeaa)DE-He213 Federated learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Real time (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 NVIDIA platforms (dpeaa)DE-He213 Dini, Pierpaolo verfasserin aut Saponara, Sergio verfasserin aut Zheng, Qinghe verfasserin aut Enthalten in Journal of real-time image processing Springer Berlin Heidelberg, 2006 21(2024), 4 vom: 26. Juni (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:21 year:2024 number:4 day:26 month:06 https://dx.doi.org/10.1007/s11554-024-01500-1 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2024 4 26 06 |
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Enthalten in Journal of real-time image processing 21(2024), 4 vom: 26. Juni volume:21 year:2024 number:4 day:26 month:06 |
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Stroke detection Federated learning Deep learning Real time Healthcare NVIDIA platforms |
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Elhanashi, Abdussalam @@aut@@ Dini, Pierpaolo @@aut@@ Saponara, Sergio @@aut@@ Zheng, Qinghe @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR056370822</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240828075021.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240627s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11554-024-01500-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR056370822</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11554-024-01500-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Elhanashi, Abdussalam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. 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Elhanashi, Abdussalam |
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620 004 VZ TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices Stroke detection (dpeaa)DE-He213 Federated learning (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Real time (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 NVIDIA platforms (dpeaa)DE-He213 |
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telestroke: real-time stroke detection with federated learning and yolov8 on edge devices |
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TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices |
| abstract |
Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field. © The Author(s) 2024 |
| abstractGer |
Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field. © The Author(s) 2024 |
| abstract_unstemmed |
Abstract Stroke, a life-threatening medical condition, necessitates immediate intervention for optimal outcomes. Timely diagnosis and treatment play a crucial role in reducing mortality and minimizing long-term disabilities associated with strokes. This study presents a novel approach to meet these critical needs by proposing a real-time stroke detection system based on deep learning (DL) with utilization of federated learning (FL) to enhance accuracy and privacy preservation. The primary objective of this research is to develop an efficient and accurate model capable of discerning between stroke and non-stroke cases in real-time, facilitating healthcare professionals in making well-informed decisions. Traditional stroke detection methods relying on manual interpretation of medical images are time-consuming and prone to human error. DL techniques have shown promise in automating this process, yet challenges persist due to the need for extensive and diverse datasets and privacy concerns. To address these challenges, our methodology involves utilization and assessing YOLOv8 models on comprehensive datasets comprising both stroke and non-stroke based on the facial paralysis of the individuals from the images. This training process empowers the model to grasp intricate patterns and features associated with strokes, thereby enhancing its diagnostic accuracy. In addition, federated learning, a decentralized training approach, is employed to bolster privacy while preserving model performance. This approach enables the model to learn from data distributed across various clients without compromising sensitive patient information. The proposed methodology has been implemented on NVIDIA platforms, utilizing their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, promising to save lives and elevate the quality of healthcare services in the neurology field. © The Author(s) 2024 |
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| title_short |
TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices |
| url |
https://dx.doi.org/10.1007/s11554-024-01500-1 |
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| author2 |
Dini, Pierpaolo Saponara, Sergio Zheng, Qinghe |
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Dini, Pierpaolo Saponara, Sergio Zheng, Qinghe |
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| doi_str |
10.1007/s11554-024-01500-1 |
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2024-08-28T05:54:08.483Z |
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| score |
7.1699915 |