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

dc.creatorMansour, Romany F.
dc.creatorEscorcia-Gutierrez, Jose
dc.creatorGamarra, Margarita
dc.creatorGarcia-Diaz, Vicente
dc.creatorGupta, Deepak
dc.creatorkumar, sachin
dc.date2021-06-26T16:10:01Z
dc.date2021-06-26T16:10:01Z
dc.date2021-06-21
dc.date.accessioned2023-10-03T19:43:16Z
dc.date.available2023-10-03T19:43:16Z
dc.identifier0941-0643
dc.identifier1433-3058
dc.identifierhttps://hdl.handle.net/11323/8414
dc.identifierhttps://doi.org/10.1007/s00521-021-06240-y
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9171752
dc.descriptionDue 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.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
dc.relation1. 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
dc.relation2. 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
dc.relation3. Ang LM, Seng KP (2016) Big sensor data applications in urban environments. Big Data Res 4:1–12
dc.relation4. 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
dc.relation5. 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
dc.relation6. 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
dc.relation7. 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
dc.relation8. 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
dc.relation9. 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
dc.relation10. 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
dc.relation11. 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
dc.relation12. 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
dc.relation13. 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
dc.relation14. 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
dc.relation15. 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
dc.relation16. Dwivedi A, Srivastava G, Dhar S, Singh R (2019) A decentralized privacy-preserving healthcare blockchain for IoT. Sensors 19(2):326
dc.relation17. Firouzi F (2018) Internet-of-things and big data for smarter Healthcare: from device to architecture, applications and analytics. Elsevier, Amsterdam
dc.relation18. 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
dc.relation19. Jabbar S (2017) Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wirel Commun Mobile Comput. https://doi.org/10.1155/2017/9731806
dc.relation20. Maktoubian J, Ansari K (2019) An IoT architecture for preventive maintenance of medical devices in healthcare organizations. Heal Technol 9(3):233–243
dc.relation21. 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
dc.relation22. Shakeel PM (2018) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42(10):186
dc.relation23. 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
dc.relation24. 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
dc.relation25. Sezer A, Sezer HB (2019) Capsule network-based classification of rotator cuff pathologies from MRI. Comput Electr Eng 80:106480
dc.relation26. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: 31st conference on neural information processing systems
dc.relation27. 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
dc.relation28. 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
dc.relation29. 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
dc.relation30. 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
dc.relation31. 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
dc.relation32. 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
dc.relation33. 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
dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceNeural Computing and Applications
dc.sourcehttps://link.springer.com/article/10.1007/s00521-021-06240-y
dc.subjecte-Diagnosis
dc.subjectInternet of medical things
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectIntracerebral hemorrhage
dc.titleArtificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images
dc.typePre-Publicación
dc.typehttp://purl.org/coar/resource_type/c_816b
dc.typeText
dc.typeinfo:eu-repo/semantics/preprint
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/redcol/resource_type/ARTOTR
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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