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

  • S.I: AI-based e-diagnosis
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Abstract

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.

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Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

This work is financially supported by the Ministry of Science and Higher Education of the Russian Federation (Government Order FENU-2020-0022).

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt

    Romany F. Mansour

  2. Electronic and Telecommunications Program, Universidad Autónoma del Caribe, 08001, Barranquilla, Colombia

    José Escorcia-Gutierrez

  3. Research Center—CIENS, Escuela Naval de Suboficiales A.R.C. “Barranquilla”, 08001, Barranquilla, Colombia

    José Escorcia-Gutierrez

  4. Department of Computational Science and Electronic, Universidad de la Costa, CUC, 08001, Barranquilla, Colombia

    Margarita Gamarra

  5. Department of Computer Science, School of Computer Science Engineering, University of Oviedo, Oviedo, Spain

    Vicente García Díaz

  6. Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India

    Deepak Gupta

  7. Department of Computer Science, South Ural State University (National Research University), Chelyabinsk, Russian Federation

    Sachin Kumar

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  1. Romany F. Mansour
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  2. José Escorcia-Gutierrez
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  3. Margarita Gamarra
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  4. Vicente García Díaz
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Correspondence to Sachin Kumar.

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Mansour, R.F., Escorcia-Gutierrez, J., Gamarra, M. et al. Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images. Neural Comput & Applic 35, 16037–16049 (2023). https://doi.org/10.1007/s00521-021-06240-y

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  • DOI: https://doi.org/10.1007/s00521-021-06240-y

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