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Smart system for identifying the various pathologies in MR brain image using Monkey Search based Interval Type-II Fuzzy C-Means technique

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Abstract

In the field of medicine, anomalous pathology prediction has become a major issue. Huma, instrument/device, and environmental errors have all contributed to the growth of these issues; yet, they can all be rectified with the use of the hybrid segmentation method. The main objective of this paper is to present a novel method, named as MS-IT2FCM, which targets the erroneous brain tumor diagnosis of abnormalities in many topographical Magnetic Resonance Imaging (MRI) regions. In the proposed method, we utilize the features of the Monkey Search algorithm and the Interval Type-II Fuzzy C-Means (IT2FCM) techniques. The uncertainties in the data are handled with interval type-II fuzzy numbers and search algorithm are used to optimize the results. Large datasets and the complex tumors can be easily examined and intervened upon by the developed method. Additionally, this could be a proactive measure implemented in clinical practice to benefit patients and physicians. The proposed methodology is implemented on the data set of BRATS 2018 and compare their results with the state-of-art. From the analysis, we conclude the results are better than those of the standard strategy in terms of predicting different diseases in MR brain imaging. The proposed method offers a clear distinction between the tumor and non-tumor portions (edema) and this clause may be included in medical pre-planning at all times.

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

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Acknowledgements

Sincere gratitude is extended by the author(s) to Dr. K.G. Srinivasan, MD, RD, Consultant Radiologist, and Dr. K.P. Usha Nandhini, DNB, KGS Advanced MR & CT Scan—Madurai, Tamilnadu, India, for providing patient data and trustworthy assistance for the practical confirmation of the suggested technique”.

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Authors and Affiliations

  1. Department of Mathematics, Thapar Institute of Engineering & Technology (Deemed University), Patiala, 147004, Punjab, India

    Harish Garg

  2. Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai (OMR), Kalavakkam, 603 110, Tamil Nadu, India

    Saravanan Alagarsamy

  3. Department of Mathematics, Rajalakshmi Institute of Technology, Chennai, India

    D. Nagarajan

  4. Department of Mechanical Engineering, Aarupadai Veedu Institute of Technology, Chennai, India

    A. Senthilkumar

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  1. Harish Garg
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  2. Saravanan Alagarsamy
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  4. A. Senthilkumar
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Correspondence to Harish Garg.

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Garg, H., Alagarsamy, S., Nagarajan, D. et al. Smart system for identifying the various pathologies in MR brain image using Monkey Search based Interval Type-II Fuzzy C-Means technique. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18808-6

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