Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images

Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease which affects the injury of blood vessels in the brain regions. This paper p...

Full description

Autores:
Mansour, Romany F.
Escorcia-Gutierrez, Jose
Gamarra, Margarita
Garcia-Diaz, Vicente
Gupta, Deepak
kumar, sachin
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8414
Acceso en línea:
https://hdl.handle.net/11323/8414
https://doi.org/10.1007/s00521-021-06240-y
https://repositorio.cuc.edu.co/
Palabra clave:
e-Diagnosis
Internet of medical things
Artificial intelligence
Deep learning
Intracerebral hemorrhage
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_ec7f10020af2005b24f5c7938a6ab1c6
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8414
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
title Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
spellingShingle Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
e-Diagnosis
Internet of medical things
Artificial intelligence
Deep learning
Intracerebral hemorrhage
title_short Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
title_full Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
title_fullStr Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
title_full_unstemmed Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
title_sort Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
dc.creator.fl_str_mv Mansour, Romany F.
Escorcia-Gutierrez, Jose
Gamarra, Margarita
Garcia-Diaz, Vicente
Gupta, Deepak
kumar, sachin
dc.contributor.author.spa.fl_str_mv Mansour, Romany F.
Escorcia-Gutierrez, Jose
Gamarra, Margarita
Garcia-Diaz, Vicente
Gupta, Deepak
kumar, sachin
dc.subject.spa.fl_str_mv e-Diagnosis
Internet of medical things
Artificial intelligence
Deep learning
Intracerebral hemorrhage
topic e-Diagnosis
Internet of medical things
Artificial intelligence
Deep learning
Intracerebral hemorrhage
description Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease which affects the injury of blood vessels in the brain regions. This paper presents an artificial intelligence and big data analytics-based ICH e-diagnosis (AIBDA-ICH) model using CT images. The presented model utilizes IoMT devices for data acquisition process. The presented AIBDA-ICH model involves graph cut-based segmentation model for identifying the affected regions in the CT images. To manage big data, Hadoop Ecosystem and its elements are mainly used. In addition, capsule network (CapsNet) model is applied as a feature extractor to derive a useful set of feature vectors. Finally, the presented AIBDA-ICH model makes use of the fuzzy deep neural network (FDNN) model to carry out classification process. For validating the superior performance of the AIBDA-ICH method, an extensive set of simulations were performed and the outcomes are examined under diverse aspects. The experimental values pointed out the improved e-diagnostic performance of the AIBDA-ICH model over the other compared methods with the precision and accuracy of 94.96% and 98.59%, respectively.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-06-26T16:10:01Z
dc.date.available.none.fl_str_mv 2021-06-26T16:10:01Z
dc.date.issued.none.fl_str_mv 2021-06-21
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv Text
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 0941-0643
1433-3058
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8414
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/s00521-021-06240-y
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0941-0643
1433-3058
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8414
https://doi.org/10.1007/s00521-021-06240-y
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Redondi A, Chirico M, Borsani L, Cesana M, Tagliasacchi M (2013) An integrated system based on wireless sensor networks for patient monitoring, localization and tracking. Ad Hoc Netw 11(1):39–53
2. Chen H, Khan S, Kou B, Nazir S, Liu W, Hussain A (2020) A smart machine learning model for the detection of brain hemorrhage diagnosis based internet of things in smart cities. Complexity. https://doi.org/10.1155/2020/3047869
3. Ang LM, Seng KP (2016) Big sensor data applications in urban environments. Big Data Res 4:1–12
4. Chang PD, Kuoy E, Grinband J, Weinberg BD, Thompson M, Homo R, Filippi CG (2018) Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. Am J Neuroradiol 39(9):1609–1616
5. Majumdar A, Brattain L, Telfer B, Farris C, Scalera J (2018) Detecting intracranial hemorrhage with deep learning. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp 583–587). IEEE
6. Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, White RD (2017) Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285:923–931
7. Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) RADnet: radiologist level accuracy using deep learning for hemorrhage detection in CT scans. In Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, 4–7 April 2018, pp 281–284
8. Ye H, Gao F, Yin Y, Guo D, Zhao P, Lu Y, Wang X, Bai J, Cao K, Song Q, Zhang H, Chen W, Guo X, Xia J (2019) Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur Radiol 29:6191–6201
9. Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, Ebert SA, Pomerantz SR, Romero JM, Kamalian S, Gonzalez RG, Lev MH, Do S (2019) An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 3:173–182
10. Jnawali K, Arbabshirani MR, Rao N, Patel AA (2018) Deep 3D convolution neural network for CT brain hemorrhage classification. In: Medical imaging 2018: computer-aided diagnosis; International Society for Optics and Photonics: Washington, DC, USA, 2018, volume 10575, p 105751C
11. Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ (2018) Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med 1:9
12. Chang PD, Kuoy E, Grinband J, Weinberg BD, Thompson M, Homo R, Chen J, Abcede H, Shafie M, Sugrue L, Filippi CG (2018) Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. Am J Neuroradiol 39(9):1609–1616
13. Majumdar A, Brattain L, Telfer B, Farris C, Scalera J (2018) Detecting intracranial hemorrhage with deep learning. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 583–587
14. Abdelaziz A (2019) A machine learning model for predicting of chronic kidney disease based internet of things and cloud computing in smart cities. Security in smart cities: models, applications, and challenges. Springer, Berlin, pp 93–114
15. Al-Majeed SS, Al-Mejibli IS, Karam J (2015) Home telehealth by internet of things (IoT). In: Proceedings of the 2015 IEEE 28th Canadian conference on electrical and computer engineering (CCECE), Halifax, Canada
16. Dwivedi A, Srivastava G, Dhar S, Singh R (2019) A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19(2):326
17. Firouzi F (2018) Internet-of-things and big data for smarter Healthcare: from device to architecture, applications and analytics. Elsevier, Amsterdam
18. Hassanalieragh M (2015) Health monitoring and management using Internet-of-things (IoT) sensing with cloud-based processing: opportunities and challenges. In: Proceedings of the 2015 IEEE international conference on services computing, New York, NY, USA
19. Jabbar S (2017) Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wirel Commun Mobile Comput. https://doi.org/10.1155/2017/9731806
20. Maktoubian J, Ansari K (2019) An IoT architecture for preventive maintenance of medical devices in healthcare organizations. Heal Technol 9(3):233–243
21. Mutlag AA, Abd Ghani MK, Arunkumar N, Mohammed MA, Mohd O (2019) Enabling technologies for fog computing in healthcare IoT systems. Future Gener Comput Syst 90:62–78
22. Shakeel PM (2018) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42(10):186
23. Selvi RT, Muthulakshmi I (2020) Modelling the map reduce based optimal gradient boosted tree classification algorithm for diabetes mellitus diagnosis system. J Ambient Intell Human Comput, pp 1–14
24. Zheng Q, Li H, Fan B, Wu S, Xu J (2018) Integrating support vector machine and graph cuts for medical image segmentation. J Vis Commun Image Represent 55:157–165
25. Sezer A, Sezer HB (2019) Capsule network-based classification of rotator cuff pathologies from MRI. Comput Electr Eng 80:106480
26. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: 31st conference on neural information processing systems
27. Deng Y, Ren Z, Kong Y, Bao F, Dai Q (2016) A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans Fuzzy Syst 25(4):1006–1012
28. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 689–696
29. Anupama CSS, Sivaram M, Lydia EL, Gupta D, Shankar K (2020) Synergic deep learning model-based automated detection and classification of brain intracranial hemorrhage images in wearable networks. Pers Ubiquit Comput. https://doi.org/10.1007/s00779-020-01492-2
30. Hssayeni MD, Croock MS, Salman AD, Al-khafaji HF, Yahya ZA, Ghoraani B (2020) Intracranial hemorrhage segmentation using a deep convolutional model. Data 5(1):14
31. Davis V, Devane S (2017) Diagnosis and classification of brain hemorrhage. In: 2017 international conference on advances in computing, communication and control (ICAC3). IEEE, pp 1–6
32. Danilov G, Kotik K, Negreeva A, Tsukanova T, Shifrin M, Zakharova N, Batalov A, Pronin I, Potapov A (2020) Classification of intracranial hemorrhage subtypes using deep learning on CT scans. Stud Health Technol Inform 272:370–373
33. Karki M, Cho J, Lee E, Hahm MH, Yoon SY, Kim M, Ahn JY, Son J, Park SH, Kim KH, Park S (2020) CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings. Artif Intell Med 106:101850
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spelling Mansour, Romany F.add7b11391c69373c38bc30f68a36bf0Escorcia-Gutierrez, Jose99d571e1577972ff2099e8b100c38559Gamarra, Margarita7754112a969e290336bee9412aa2822eGarcia-Diaz, Vicente7385b53b82618fa18b0112aaa18e98a2Gupta, Deepak5137b984925651a7a8ef9a5a78fdcefbkumar, sachina60ca4cfd894241019c8351122ca00be2021-06-26T16:10:01Z2021-06-26T16:10:01Z2021-06-210941-06431433-3058https://hdl.handle.net/11323/8414https://doi.org/10.1007/s00521-021-06240-yCorporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease which affects the injury of blood vessels in the brain regions. This paper presents an artificial intelligence and big data analytics-based ICH e-diagnosis (AIBDA-ICH) model using CT images. The presented model utilizes IoMT devices for data acquisition process. The presented AIBDA-ICH model involves graph cut-based segmentation model for identifying the affected regions in the CT images. To manage big data, Hadoop Ecosystem and its elements are mainly used. In addition, capsule network (CapsNet) model is applied as a feature extractor to derive a useful set of feature vectors. Finally, the presented AIBDA-ICH model makes use of the fuzzy deep neural network (FDNN) model to carry out classification process. For validating the superior performance of the AIBDA-ICH method, an extensive set of simulations were performed and the outcomes are examined under diverse aspects. The experimental values pointed out the improved e-diagnostic performance of the AIBDA-ICH model over the other compared methods with the precision and accuracy of 94.96% and 98.59%, respectively.application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Neural Computing and Applicationshttps://link.springer.com/article/10.1007/s00521-021-06240-ye-DiagnosisInternet of medical thingsArtificial intelligenceDeep learningIntracerebral hemorrhageArtificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT imagesPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersion1. Redondi A, Chirico M, Borsani L, Cesana M, Tagliasacchi M (2013) An integrated system based on wireless sensor networks for patient monitoring, localization and tracking. Ad Hoc Netw 11(1):39–532. Chen H, Khan S, Kou B, Nazir S, Liu W, Hussain A (2020) A smart machine learning model for the detection of brain hemorrhage diagnosis based internet of things in smart cities. Complexity. https://doi.org/10.1155/2020/30478693. Ang LM, Seng KP (2016) Big sensor data applications in urban environments. Big Data Res 4:1–124. Chang PD, Kuoy E, Grinband J, Weinberg BD, Thompson M, Homo R, Filippi CG (2018) Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. Am J Neuroradiol 39(9):1609–16165. Majumdar A, Brattain L, Telfer B, Farris C, Scalera J (2018) Detecting intracranial hemorrhage with deep learning. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp 583–587). IEEE6. Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, White RD (2017) Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285:923–9317. Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) RADnet: radiologist level accuracy using deep learning for hemorrhage detection in CT scans. In Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, 4–7 April 2018, pp 281–2848. Ye H, Gao F, Yin Y, Guo D, Zhao P, Lu Y, Wang X, Bai J, Cao K, Song Q, Zhang H, Chen W, Guo X, Xia J (2019) Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur Radiol 29:6191–62019. Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, Ebert SA, Pomerantz SR, Romero JM, Kamalian S, Gonzalez RG, Lev MH, Do S (2019) An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 3:173–18210. Jnawali K, Arbabshirani MR, Rao N, Patel AA (2018) Deep 3D convolution neural network for CT brain hemorrhage classification. In: Medical imaging 2018: computer-aided diagnosis; International Society for Optics and Photonics: Washington, DC, USA, 2018, volume 10575, p 105751C11. Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ (2018) Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med 1:912. Chang PD, Kuoy E, Grinband J, Weinberg BD, Thompson M, Homo R, Chen J, Abcede H, Shafie M, Sugrue L, Filippi CG (2018) Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. Am J Neuroradiol 39(9):1609–161613. Majumdar A, Brattain L, Telfer B, Farris C, Scalera J (2018) Detecting intracranial hemorrhage with deep learning. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 583–58714. Abdelaziz A (2019) A machine learning model for predicting of chronic kidney disease based internet of things and cloud computing in smart cities. Security in smart cities: models, applications, and challenges. Springer, Berlin, pp 93–11415. Al-Majeed SS, Al-Mejibli IS, Karam J (2015) Home telehealth by internet of things (IoT). In: Proceedings of the 2015 IEEE 28th Canadian conference on electrical and computer engineering (CCECE), Halifax, Canada16. Dwivedi A, Srivastava G, Dhar S, Singh R (2019) A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19(2):32617. Firouzi F (2018) Internet-of-things and big data for smarter Healthcare: from device to architecture, applications and analytics. Elsevier, Amsterdam18. Hassanalieragh M (2015) Health monitoring and management using Internet-of-things (IoT) sensing with cloud-based processing: opportunities and challenges. In: Proceedings of the 2015 IEEE international conference on services computing, New York, NY, USA19. Jabbar S (2017) Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wirel Commun Mobile Comput. https://doi.org/10.1155/2017/973180620. Maktoubian J, Ansari K (2019) An IoT architecture for preventive maintenance of medical devices in healthcare organizations. Heal Technol 9(3):233–24321. Mutlag AA, Abd Ghani MK, Arunkumar N, Mohammed MA, Mohd O (2019) Enabling technologies for fog computing in healthcare IoT systems. Future Gener Comput Syst 90:62–7822. Shakeel PM (2018) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42(10):18623. Selvi RT, Muthulakshmi I (2020) Modelling the map reduce based optimal gradient boosted tree classification algorithm for diabetes mellitus diagnosis system. J Ambient Intell Human Comput, pp 1–1424. Zheng Q, Li H, Fan B, Wu S, Xu J (2018) Integrating support vector machine and graph cuts for medical image segmentation. J Vis Commun Image Represent 55:157–16525. Sezer A, Sezer HB (2019) Capsule network-based classification of rotator cuff pathologies from MRI. Comput Electr Eng 80:10648026. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: 31st conference on neural information processing systems27. Deng Y, Ren Z, Kong Y, Bao F, Dai Q (2016) A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans Fuzzy Syst 25(4):1006–101228. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 689–69629. Anupama CSS, Sivaram M, Lydia EL, Gupta D, Shankar K (2020) Synergic deep learning model-based automated detection and classification of brain intracranial hemorrhage images in wearable networks. Pers Ubiquit Comput. https://doi.org/10.1007/s00779-020-01492-230. Hssayeni MD, Croock MS, Salman AD, Al-khafaji HF, Yahya ZA, Ghoraani B (2020) Intracranial hemorrhage segmentation using a deep convolutional model. Data 5(1):1431. Davis V, Devane S (2017) Diagnosis and classification of brain hemorrhage. In: 2017 international conference on advances in computing, communication and control (ICAC3). IEEE, pp 1–632. Danilov G, Kotik K, Negreeva A, Tsukanova T, Shifrin M, Zakharova N, Batalov A, Pronin I, Potapov A (2020) Classification of intracranial hemorrhage subtypes using deep learning on CT scans. Stud Health Technol Inform 272:370–37333. Karki M, Cho J, Lee E, Hahm MH, Yoon SY, Kim M, Ahn JY, Son J, Park SH, Kim KH, Park S (2020) CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings. 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