Fuzzy-Based Optimization Techniques for Segmenting the Tumors in Multimodal MRI Images

Saravanan Alagarsamy (), D. Nagarajan () and Vishnuvardhan Govindaraj ()
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Saravanan Alagarsamy: Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai (OMR)
D. Nagarajan: Rajalakshmi Institute of Technology
Vishnuvardhan Govindaraj: VIT Bhopal University

SN Operations Research Forum, 2025, vol. 6, issue 1, 1-27

Abstract: Abstract Accurate brain tumor prediction is essential when approaching the field of health care; wherever accuracy in decision-making is crucial, the issues also need to be resolved right away. Many artificial intelligence and machine learning-based techniques have been developed in the last couple of years in the field of healthcare. The intention of this work is to create an algorithm that combines the features of Crow Search (CS) and Interval Type-II Fuzzy Logic System (IT2FLS) algorithms to distinguish the area of tumor from complex brain tissues. The ability of oncologists to make decisions is critical for any therapy sequence to be successful, and the methodology proposed in the research work considerably influences a conclusion through technology interruption. The suggested approach is flexible enough to work with a diversity of image sequences found in the BRATS Challenge 2020 dataset that presents varying degrees of obstacles, challenges, and difficulties in locating the anomalous regions, and it produces improved demarcation results that have been instinctively evaluated and supported by Dice score (98 ± 1.3), specificity (98 ± 1.4), and sensitivity (98 ± 1.7) as average metrics. The motive of this study is the enhancement of the visual perception of oncologists, which gives them improved insight into and comprehension of the condition of the patient.

Keywords: Magnetic Resonance Imaging (MRI); Crow Search (CS); Interval Type-II Fuzzy Logic System (IT2FLS); Tumor segmentation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00433-0

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