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A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians

  • Recent Advances in Deep Learning for Medical Image Processing
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Abstract

The human brain is considered to be the anatomical seat of intelligence, comprehensively supervising conscious and autonomous functions responsible for monitoring and control operations. Although neural homeostasis can be disrupted, early signs of disease should be recognized to save the patient from permanent disability and even a preventable death. The record of World Health Organization (WHO) lists various brain diseases, such as aneurism, stroke and tumor, which affect humans irrespective of their age, sex and province, all of which affect diagnosis, prognosis and treatment options. Since clinically significant diagnosis of brain abnormality is generally performed using dedicated imaging procedures and also under the supervision of an experienced radiologist, more accurate tools can make this process even more precise. The usual protocol involves a radiologist who records the three-dimensional (3D) image which provides initial insight on the type of brain disease, followed by doctor examination of the 3D/2D image that determines the treatment plan. This article proposes a tool and associated procedure to examine a clinical brain image with improved accuracy in order to provide early insight on ideal treatment procedure. In summary, this tool gives the treatment team unprecedented assessment capability before an operation by integrating all the possible image processing procedures to enhance the result in brain image analysis.

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Acknowledgements

The authors of this article would like to acknowledge M/S. Proscans Diagnostics Pvt. Ltd., a leading scan center in Chennai, for providing the clinical brain MRI for experimental investigation.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Sahyadri College of Engineering and Management, Mangaluru, Karnataka, 575007, India

    Steven Lawrence Fernandes

  2. Department of Computer Science and Information Systems, Texas A&M University-Commerce, Commerce, TX, 75429-3011, USA

    U. John Tanik

  3. Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, 600119, India

    V. Rajinikanth

  4. Proscan Diagnostics Private Limited, Chennai, Tamilnadu, 600049, India

    K. Arvind Karthik

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  1. Steven Lawrence Fernandes
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  2. U. John Tanik
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Corresponding author

Correspondence to V. Rajinikanth.

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Fernandes, S.L., Tanik, U.J., Rajinikanth, V. et al. A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians. Neural Comput & Applic 32, 15897–15908 (2020). https://doi.org/10.1007/s00521-019-04369-5

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