An image classification deep-learning algorithm for shrapnel detection from ultrasound images
Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image...
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| Published in: | Scientific reports Vol. 12; no. 1; pp. 8427 - 12 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Nature Publishing Group UK
19.05.2022
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce. |
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| AbstractList | Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce. Abstract Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce. Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce.Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce. |
| ArticleNumber | 8427 |
| Author | Hernandez-Torres, Sofia I. Boice, Emily N. Snider, Eric J. |
| Author_xml | – sequence: 1 givenname: Eric J. surname: Snider fullname: Snider, Eric J. email: eric.j.snider3.civ@mail.mil organization: Engineering Technology and Automation Combat Casualty Care Research Team, United States Army Institute of Surgical Research – sequence: 2 givenname: Sofia I. surname: Hernandez-Torres fullname: Hernandez-Torres, Sofia I. organization: Engineering Technology and Automation Combat Casualty Care Research Team, United States Army Institute of Surgical Research – sequence: 3 givenname: Emily N. surname: Boice fullname: Boice, Emily N. organization: Engineering Technology and Automation Combat Casualty Care Research Team, United States Army Institute of Surgical Research |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35589931$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/1905005$$D View this record in Osti.gov |
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| Cites_doi | 10.1038/s41598-018-25005-7 10.1007/s11042-018-6082-6 10.1118/1.2178451 10.1002/mrd.22489 10.3390/app11020672 10.23915/distill.00026 10.1038/s41598-020-61079-y 10.1016/j.ultrasmedbio.2020.06.015 10.2174/1573405615666191023104751 10.1109/TMI.2006.891477 10.3390/brainsci10070427 10.1038/nmeth.2019 10.1097/MD.0000000000015133 10.1109/TMI.2018.2860257 10.1016/j.patrec.2020.09.020 10.1109/ACCESS.2020.3010863 10.1007/978-3-030-61702-8_28 10.1007/978-981-15-3383-9_29 10.1109/JBHI.2021.3084962 10.1109/TMI.2017.2712367 10.1016/j.jcms.2011.09.005 10.1016/S0196-0644(97)70347-0 10.7205/MILMED-D-10-00025 10.1097/00005373-199903000-00022 10.1038/s41598-020-67076-5 10.1097/JTN.0000000000000329 10.1038/s41598-021-87910-8 10.1109/SIPROCESS.2018.8600536 10.1109/CVPR.2009.5206848 |
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| References | Shuker (CR6) 2012; 40 Khobragade, Jain, Sisodia, Florez, Misra (CR22) 2020 Buddhavarapu (CR24) 2020; 140 Li, Weng, Shi, Gu, Mao, Wang (CR12) 2018; 8 CR19 Xu, Hamilton (CR15) 2006; 33 CR39 CR38 Schindelin, Rueden, Hiner, Eliceiri (CR29) 2015; 82 CR37 Wolf, Bucknell (CR5) 2010; 175 Born, Wiedemann, Cossio, Buhre, Brändle, Leidermann (CR11) 2021; 11 Hill, Conron, Greissinger, Heller (CR3) 1997; 29 CR34 Munien, Viriri (CR23) 2021; 9 CR33 CR31 Pencil (CR8) 2017; 24 Liu, Wang, Li, Gong, Su, Zhao (CR43) 2019; 13 Wu, Tan, Zhu, Chen, Yang, Wen (CR17) 2021; 25 Gemignani, Faita, Ghiadoni, Poggianti, Demi (CR16) 2007; 26 Miglani, Bhatia, Hassanien, Bhatnagar, Darwish (CR21) 2021 Baumgartner, Kamnitsas, Matthew, Fletcher, Smith, Koch (CR13) 2017; 36 CR4 Albahli, Albattah (CR26) 2020; 28 Snider, Cornell, Acevedo, Gross, Edsall, Lund (CR44) 2020; 10 CR7 Schindelin, Arganda-Carreras, Frise, Kaynig, Longair, Pietzsch (CR30) 2012; 9 CR28 CR27 Vakanski, Xian, Freer (CR14) 2020; 46 CR25 CR47 CR46 Tammina (CR18) 2019; 6 Yu, Wang, Ma (CR10) 2020; 16 Burgos-Artizzu, Coronado-Gutiérrez, Valenzuela-Alcaraz, Bonet-Carne, Eixarch, Crispi (CR42) 2020; 10 Shi, Hao, Zhao, Feng, He, Wang (CR20) 2019; 78 Yaqub, Feng, Zia, Arshid, Jia, Rehman (CR35) 2020; 10 Zeimarani, Costa, Nurani, Bianco, De Albuquerque Pereira, Filho (CR32) 2020; 8 Radwan, Abu-Zidan (CR1) 2006; 6 CR40 Scalea, Rodriguez, Chiu, Brenneman, Fallon, Kato (CR2) 1999; 46 Snider, Boice, Butler, Gross, Zamora (CR45) 2021; 11 Chiang, Huang, Chen, Huang, Chang (CR9) 2019; 38 Song, Chai, Masuoka, Park, Kim, Choi (CR41) 2019; 98 Agnihotri, Batra (CR36) 2020; 5 J Schindelin (12367_CR29) 2015; 82 TM Scalea (12367_CR2) 1999; 46 JM Wolf (12367_CR5) 2010; 175 VG Buddhavarapu (12367_CR24) 2020; 140 EJ Snider (12367_CR44) 2020; 10 X Yu (12367_CR10) 2020; 16 XP Burgos-Artizzu (12367_CR42) 2020; 10 Z Shi (12367_CR20) 2019; 78 12367_CR28 MM Radwan (12367_CR1) 2006; 6 12367_CR27 K Pencil (12367_CR8) 2017; 24 12367_CR34 A Vakanski (12367_CR14) 2020; 46 12367_CR37 12367_CR4 12367_CR31 12367_CR7 12367_CR33 Y Liu (12367_CR43) 2019; 13 V Khobragade (12367_CR22) 2020 EJ Snider (12367_CR45) 2021; 11 M Yaqub (12367_CR35) 2020; 10 A Agnihotri (12367_CR36) 2020; 5 V Miglani (12367_CR21) 2021 T-C Chiang (12367_CR9) 2019; 38 H Li (12367_CR12) 2018; 8 J Song (12367_CR41) 2019; 98 S Tammina (12367_CR18) 2019; 6 J Born (12367_CR11) 2021; 11 Q Xu (12367_CR15) 2006; 33 12367_CR39 12367_CR38 12367_CR19 12367_CR46 R Hill (12367_CR3) 1997; 29 12367_CR25 12367_CR47 V Gemignani (12367_CR16) 2007; 26 CF Baumgartner (12367_CR13) 2017; 36 S Albahli (12367_CR26) 2020; 28 J Schindelin (12367_CR30) 2012; 9 12367_CR40 B Zeimarani (12367_CR32) 2020; 8 ST Shuker (12367_CR6) 2012; 40 X Wu (12367_CR17) 2021; 25 C Munien (12367_CR23) 2021; 9 |
| References_xml | – volume: 8 start-page: 6600 issue: 1 year: 2018 ident: CR12 article-title: An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images publication-title: Sci. Rep. doi: 10.1038/s41598-018-25005-7 – volume: 6 issue: 9 year: 2019 ident: CR18 article-title: Transfer learning using VGG-16 with deep convolutional neural network for classifying images publication-title: Int. J. Sci. Res. Publ. (IJSRP). – volume: 78 start-page: 1017 issue: 1 year: 2019 end-page: 1033 ident: CR20 article-title: A deep CNN based transfer learning method for false positive reduction publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-018-6082-6 – volume: 33 start-page: 916 issue: 4 year: 2006 end-page: 921 ident: CR15 article-title: A novel respiratory detection method based on automated analysis of ultrasound diaphragm video publication-title: Med. Phys. doi: 10.1118/1.2178451 – ident: CR47 – volume: 9 issue: 2021 year: 2021 ident: CR23 article-title: Classification of hematoxylin and eosin-stained breast cancer histology microscopy images using transfer learning with efficientnets publication-title: Comput. Intell. Neurosci. – ident: CR4 – ident: CR39 – volume: 82 start-page: 518 issue: 7–8 year: 2015 end-page: 529 ident: CR29 article-title: The ImageJ ecosystem: An open platform for biomedical image analysis publication-title: Mol. Reprod. Dev. doi: 10.1002/mrd.22489 – ident: CR37 – volume: 11 start-page: 672 issue: 2 year: 2021 ident: CR11 article-title: Accelerating detection of lung pathologies with explainable ultrasound image analysis publication-title: Appl. Sci. doi: 10.3390/app11020672 – volume: 5 issue: 5 year: 2020 ident: CR36 article-title: Exploring Bayesian optimization publication-title: Distill. doi: 10.23915/distill.00026 – volume: 13 issue: 2019 year: 2019 ident: CR43 article-title: Intraocular foreign bodies: clinical characteristics and prognostic factors influencing visual outcome and globe survival in 373 eyes publication-title: J. Ophthalmol. – ident: CR33 – volume: 10 start-page: 4218 issue: 1 year: 2020 ident: CR44 article-title: Development and characterization of a benchtop corneal puncture injury model publication-title: Sci. Rep. doi: 10.1038/s41598-020-61079-y – volume: 46 start-page: 2819 issue: 10 year: 2020 end-page: 2833 ident: CR14 article-title: Attention-enriched deep learning model for breast tumor segmentation in ultrasound images publication-title: Ultrasound Med. Biol. doi: 10.1016/j.ultrasmedbio.2020.06.015 – volume: 16 start-page: 174 issue: 2 year: 2020 end-page: 180 ident: CR10 article-title: Detection of thyroid nodules with ultrasound images based on deep learning publication-title: Curr. Med. Imaging Rev. doi: 10.2174/1573405615666191023104751 – volume: 26 start-page: 393 issue: 3 year: 2007 end-page: 404 ident: CR16 article-title: A system for real-time measurement of the brachial artery diameter in B-mode ultrasound images publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2006.891477 – volume: 10 start-page: 427 issue: 7 year: 2020 ident: CR35 article-title: State-of-the-Art CNN optimizer for brain tumor segmentation in magnetic resonance images publication-title: Brain Sci. doi: 10.3390/brainsci10070427 – ident: CR40 – ident: CR25 – ident: CR27 – volume: 9 start-page: 676 issue: 7 year: 2012 end-page: 682 ident: CR30 article-title: Fiji: An open-source platform for biological-image analysis publication-title: Nat. Methods. doi: 10.1038/nmeth.2019 – ident: CR46 – ident: CR19 – volume: 98 issue: 15 year: 2019 ident: CR41 article-title: Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules publication-title: Medicine (Baltimore) doi: 10.1097/MD.0000000000015133 – volume: 38 start-page: 240 issue: 1 year: 2019 end-page: 249 ident: CR9 article-title: Tumor detection in automated breast ultrasound using 3-D CNN and prioritized candidate aggregation publication-title: IEEE Trans. Med. Imaging. doi: 10.1109/TMI.2018.2860257 – volume: 140 start-page: 1 year: 2020 end-page: 9 ident: CR24 article-title: An experimental study on classification of thyroid histopathology images using transfer learning publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2020.09.020 – volume: 8 start-page: 133349 year: 2020 end-page: 133359 ident: CR32 article-title: Breast lesion classification in ultrasound images using deep convolutional neural network publication-title: IEEE Access. doi: 10.1109/ACCESS.2020.3010863 – start-page: 409 year: 2020 end-page: 419 ident: CR22 article-title: Deep transfer learning model for automated screening of cervical cancer cells using multi-cell images publication-title: Applied Informatics doi: 10.1007/978-3-030-61702-8_28 – ident: CR38 – start-page: 315 year: 2021 end-page: 324 ident: CR21 article-title: Skin lesion classification: a transfer learning approach using efficientnets publication-title: Advanced Machine Learning Technologies and Applications doi: 10.1007/978-981-15-3383-9_29 – volume: 25 start-page: 3812 issue: 10 year: 2021 end-page: 3823 ident: CR17 article-title: CacheTrack-YOLO: Real-time detection and tracking for thyroid nodules and surrounding tissues in ultrasound videos publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2021.3084962 – volume: 36 start-page: 2204 issue: 11 year: 2017 end-page: 2215 ident: CR13 article-title: SonoNet: Real-time detection and localisation of fetal standard scan planes in freehand ultrasound publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2017.2712367 – ident: CR31 – volume: 40 start-page: 534 issue: 6 year: 2012 end-page: 540 ident: CR6 article-title: The immediate lifesaving management of maxillofacial, life-threatening haemorrhages due to IED and/or shrapnel injuries: “when hazard is in hesitation, not in the action” publication-title: J. Craniomaxillofac. Surg. doi: 10.1016/j.jcms.2011.09.005 – volume: 29 start-page: 353 issue: 3 year: 1997 end-page: 356 ident: CR3 article-title: Ultrasound for the detection of foreign bodies in human tissue publication-title: Ann. Emerg. Med. doi: 10.1016/S0196-0644(97)70347-0 – volume: 175 start-page: 742 issue: 10 year: 2010 end-page: 744 ident: CR5 article-title: Arthroscopic removal of improvised explosive device (IED) debris from the wrist: A case report publication-title: Mil. Med. doi: 10.7205/MILMED-D-10-00025 – volume: 6 start-page: 187 issue: 3 year: 2006 end-page: 190 ident: CR1 article-title: Focussed assessment sonograph trauma (FAST) and CT scan in blunt abdominal trauma: Surgeon’s perspective publication-title: Afr. Health Sci. – ident: CR34 – volume: 28 start-page: 841 issue: 5 year: 2020 end-page: 850 ident: CR26 article-title: Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms publication-title: J. Xray Sci. Technol. – volume: 46 start-page: 466 issue: 3 year: 1999 end-page: 472 ident: CR2 article-title: Focused assessment with sonography for trauma (FAST): Results from an international consensus conference publication-title: J. Trauma. doi: 10.1097/00005373-199903000-00022 – volume: 10 start-page: 10200 issue: 1 year: 2020 ident: CR42 article-title: Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes publication-title: Sci. Rep. doi: 10.1038/s41598-020-67076-5 – ident: CR7 – volume: 24 start-page: 376 issue: 6 year: 2017 end-page: 380 ident: CR8 article-title: eFAST simulation training for trauma providers publication-title: J. Trauma Nurs. doi: 10.1097/JTN.0000000000000329 – ident: CR28 – volume: 11 start-page: 8546 issue: 1 year: 2021 ident: CR45 article-title: Characterization of an anterior segment organ culture model for open globe injuries publication-title: Sci. Rep. doi: 10.1038/s41598-021-87910-8 – volume: 5 issue: 5 year: 2020 ident: 12367_CR36 publication-title: Distill. doi: 10.23915/distill.00026 – ident: 12367_CR38 – ident: 12367_CR40 – volume: 8 start-page: 6600 issue: 1 year: 2018 ident: 12367_CR12 publication-title: Sci. Rep. doi: 10.1038/s41598-018-25005-7 – volume: 26 start-page: 393 issue: 3 year: 2007 ident: 12367_CR16 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2006.891477 – volume: 9 issue: 2021 year: 2021 ident: 12367_CR23 publication-title: Comput. Intell. Neurosci. – ident: 12367_CR46 – ident: 12367_CR19 doi: 10.1109/SIPROCESS.2018.8600536 – volume: 46 start-page: 466 issue: 3 year: 1999 ident: 12367_CR2 publication-title: J. Trauma. doi: 10.1097/00005373-199903000-00022 – volume: 29 start-page: 353 issue: 3 year: 1997 ident: 12367_CR3 publication-title: Ann. Emerg. Med. doi: 10.1016/S0196-0644(97)70347-0 – volume: 82 start-page: 518 issue: 7–8 year: 2015 ident: 12367_CR29 publication-title: Mol. Reprod. Dev. doi: 10.1002/mrd.22489 – volume: 11 start-page: 8546 issue: 1 year: 2021 ident: 12367_CR45 publication-title: Sci. Rep. doi: 10.1038/s41598-021-87910-8 – volume: 16 start-page: 174 issue: 2 year: 2020 ident: 12367_CR10 publication-title: Curr. Med. Imaging Rev. doi: 10.2174/1573405615666191023104751 – start-page: 409 volume-title: Applied Informatics year: 2020 ident: 12367_CR22 doi: 10.1007/978-3-030-61702-8_28 – ident: 12367_CR25 – volume: 40 start-page: 534 issue: 6 year: 2012 ident: 12367_CR6 publication-title: J. Craniomaxillofac. Surg. doi: 10.1016/j.jcms.2011.09.005 – volume: 25 start-page: 3812 issue: 10 year: 2021 ident: 12367_CR17 publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2021.3084962 – ident: 12367_CR27 doi: 10.1109/CVPR.2009.5206848 – volume: 13 issue: 2019 year: 2019 ident: 12367_CR43 publication-title: J. Ophthalmol. – ident: 12367_CR7 – ident: 12367_CR34 – volume: 28 start-page: 841 issue: 5 year: 2020 ident: 12367_CR26 publication-title: J. Xray Sci. Technol. – volume: 6 start-page: 187 issue: 3 year: 2006 ident: 12367_CR1 publication-title: Afr. Health Sci. – ident: 12367_CR39 – ident: 12367_CR37 – volume: 36 start-page: 2204 issue: 11 year: 2017 ident: 12367_CR13 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2017.2712367 – volume: 78 start-page: 1017 issue: 1 year: 2019 ident: 12367_CR20 publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-018-6082-6 – volume: 10 start-page: 4218 issue: 1 year: 2020 ident: 12367_CR44 publication-title: Sci. Rep. doi: 10.1038/s41598-020-61079-y – start-page: 315 volume-title: Advanced Machine Learning Technologies and Applications year: 2021 ident: 12367_CR21 doi: 10.1007/978-981-15-3383-9_29 – volume: 24 start-page: 376 issue: 6 year: 2017 ident: 12367_CR8 publication-title: J. Trauma Nurs. doi: 10.1097/JTN.0000000000000329 – volume: 140 start-page: 1 year: 2020 ident: 12367_CR24 publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2020.09.020 – volume: 98 issue: 15 year: 2019 ident: 12367_CR41 publication-title: Medicine (Baltimore) doi: 10.1097/MD.0000000000015133 – volume: 10 start-page: 427 issue: 7 year: 2020 ident: 12367_CR35 publication-title: Brain Sci. doi: 10.3390/brainsci10070427 – volume: 46 start-page: 2819 issue: 10 year: 2020 ident: 12367_CR14 publication-title: Ultrasound Med. Biol. doi: 10.1016/j.ultrasmedbio.2020.06.015 – volume: 8 start-page: 133349 year: 2020 ident: 12367_CR32 publication-title: IEEE Access. doi: 10.1109/ACCESS.2020.3010863 – volume: 33 start-page: 916 issue: 4 year: 2006 ident: 12367_CR15 publication-title: Med. Phys. doi: 10.1118/1.2178451 – volume: 11 start-page: 672 issue: 2 year: 2021 ident: 12367_CR11 publication-title: Appl. Sci. doi: 10.3390/app11020672 – volume: 10 start-page: 10200 issue: 1 year: 2020 ident: 12367_CR42 publication-title: Sci. Rep. doi: 10.1038/s41598-020-67076-5 – ident: 12367_CR28 – volume: 38 start-page: 240 issue: 1 year: 2019 ident: 12367_CR9 publication-title: IEEE Trans. Med. Imaging. doi: 10.1109/TMI.2018.2860257 – ident: 12367_CR47 – ident: 12367_CR33 – volume: 175 start-page: 742 issue: 10 year: 2010 ident: 12367_CR5 publication-title: Mil. Med. doi: 10.7205/MILMED-D-10-00025 – volume: 6 issue: 9 year: 2019 ident: 12367_CR18 publication-title: Int. J. Sci. Res. Publ. (IJSRP). – volume: 9 start-page: 676 issue: 7 year: 2012 ident: 12367_CR30 publication-title: Nat. Methods. doi: 10.1038/nmeth.2019 – ident: 12367_CR31 – ident: 12367_CR4 |
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| Title | An image classification deep-learning algorithm for shrapnel detection from ultrasound images |
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