Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN
<p dir="ltr">Trauma is a major cause of disability among children, requiring swift and accurate diagnosis for effective treatment. This paper introduces an automated method that uses deep learning to detect and categorize fractures in children using X-ray images. The system makes use...
محفوظ في:
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513523575422976 |
|---|---|
| author | Promit Basak (22997776) |
| author2 | Adam Mushtak (22048013) Mohamed Ouda (21394001) Sadia Farhana Nobi (21512813) Anwarul Hasan (1332066) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author |
| author_facet | Promit Basak (22997776) Adam Mushtak (22048013) Mohamed Ouda (21394001) Sadia Farhana Nobi (21512813) Anwarul Hasan (1332066) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Promit Basak (22997776) Adam Mushtak (22048013) Mohamed Ouda (21394001) Sadia Farhana Nobi (21512813) Anwarul Hasan (1332066) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2025-09-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-025-11539-1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Improving_pediatric_trauma_care_an_automated_system_for_wrist_trauma_detection_using_GELAN/31289185 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Computer vision and multimedia computation Machine learning Pediatric trauma Fracture detection Deep learning X-ray Medical imaging Generalized efficient layer aggregation network (GELAN) |
| dc.title.none.fl_str_mv | Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Trauma is a major cause of disability among children, requiring swift and accurate diagnosis for effective treatment. This paper introduces an automated method that uses deep learning to detect and categorize fractures in children using X-ray images. The system makes use of the GRAZPEDWRI-DX dataset, which consists of 20,327 annotated X-ray images of pediatric wrist fractures. Our architecture, which is built upon the generalized efficient layer aggregation network (GELAN), effectively tackles the issues of class imbalance and image resolution. As a result, it achieves state-of-the-art performance in both trauma and severity detection. Our proposed framework surpassed the most advanced techniques, showcasing exceptional precision and effectiveness, achieving a mean average precision (mAP50) score of 74.1%, 95%, and 85.5% for Task A (trauma detection), Task B (fracture detection), and Task C (fracture severity detection), respectively. The results of our study highlight the capacity of deep learning to improve the diagnosis of pediatric trauma, decrease the burden on radiologists, and boost patient outcomes.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-025-11539-1" target="_blank">https://dx.doi.org/10.1007/s00521-025-11539-1</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d443732a19721711c0dba1ba7b5a6776 |
| identifier_str_mv | 10.1007/s00521-025-11539-1 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/31289185 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Improving pediatric trauma care: an automated system for wrist trauma detection using GELANPromit Basak (22997776)Adam Mushtak (22048013)Mohamed Ouda (21394001)Sadia Farhana Nobi (21512813)Anwarul Hasan (1332066)Muhammad E. H. Chowdhury (14150526)Biomedical and clinical sciencesClinical sciencesHealth sciencesHealth services and systemsInformation and computing sciencesComputer vision and multimedia computationMachine learningPediatric traumaFracture detectionDeep learningX-rayMedical imagingGeneralized efficient layer aggregation network (GELAN)<p dir="ltr">Trauma is a major cause of disability among children, requiring swift and accurate diagnosis for effective treatment. This paper introduces an automated method that uses deep learning to detect and categorize fractures in children using X-ray images. The system makes use of the GRAZPEDWRI-DX dataset, which consists of 20,327 annotated X-ray images of pediatric wrist fractures. Our architecture, which is built upon the generalized efficient layer aggregation network (GELAN), effectively tackles the issues of class imbalance and image resolution. As a result, it achieves state-of-the-art performance in both trauma and severity detection. Our proposed framework surpassed the most advanced techniques, showcasing exceptional precision and effectiveness, achieving a mean average precision (mAP50) score of 74.1%, 95%, and 85.5% for Task A (trauma detection), Task B (fracture detection), and Task C (fracture severity detection), respectively. The results of our study highlight the capacity of deep learning to improve the diagnosis of pediatric trauma, decrease the burden on radiologists, and boost patient outcomes.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-025-11539-1" target="_blank">https://dx.doi.org/10.1007/s00521-025-11539-1</a></p>2025-09-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-025-11539-1https://figshare.com/articles/journal_contribution/Improving_pediatric_trauma_care_an_automated_system_for_wrist_trauma_detection_using_GELAN/31289185CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/312891852025-09-15T03:00:00Z |
| spellingShingle | Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN Promit Basak (22997776) Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Computer vision and multimedia computation Machine learning Pediatric trauma Fracture detection Deep learning X-ray Medical imaging Generalized efficient layer aggregation network (GELAN) |
| status_str | publishedVersion |
| title | Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN |
| title_full | Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN |
| title_fullStr | Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN |
| title_full_unstemmed | Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN |
| title_short | Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN |
| title_sort | Improving pediatric trauma care: an automated system for wrist trauma detection using GELAN |
| topic | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Information and computing sciences Computer vision and multimedia computation Machine learning Pediatric trauma Fracture detection Deep learning X-ray Medical imaging Generalized efficient layer aggregation network (GELAN) |