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Review and potential for artificial intelligence in healthcare

Author

Listed:
  • Lina Sun

    (North China Institute of Science and Technology)

  • Rajiv Kumar Gupta

    (Pandit Deendayal Energy University)

  • Amit Sharma

    (Chitkara University)

Abstract

In the medical image analysis, recognition of tumor in brain is very important task and it leads cancer which should be diagnosed at early stage. It is an irregular cell population in brain and for the cancer diagnosis; medical imaging techniques play an important role. The mostly used and efficient technique for the segmentation is Magnetic resonance imaging (MRI). There is huge progress the field of MRI imaging technique for accessing the brain injury and the brain anatomy exploring. The segmentation and detection of the tumor from the MRI images are done by the image processing techniques. Manual detection of brain tumor is the complex task, so the different image segmentation methods are developed for detection and segmentation of the tumor from the MRI images. The various recent brain tumor segmentation techniques are thoroughly discussed in this paper. The quantitative analysis of existing techniques and the performance evaluation is done and detailed. The paper revealed different image segmentation methods are briefly discussed. This survey article provides the detailed information of the different segmentation methods along with their merits and demerits. Effectiveness of the methods is shown in terms of the performance parameters.

Suggested Citation

  • Lina Sun & Rajiv Kumar Gupta & Amit Sharma, 2022. "Review and potential for artificial intelligence in healthcare," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 54-62, March.
  • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01221-9
    DOI: 10.1007/s13198-021-01221-9
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    Cited by:

    1. Amit Sharma & Ashutosh Sharma & Polina Nikashina & Vadim Gavrilenko & Alexey Tselykh & Alexander Bozhenyuk & Mehedi Masud & Hossam Meshref, 2023. "A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities," Sustainability, MDPI, vol. 15(15), pages 1-25, August.

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