Affiliation:
1. Department of Instrumentation and Control Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India
2. Department of Biomedical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India
3. Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India
4. Department of Computer Science and Engineering, Muthoot Institute of Technology& Science Cochin India
Abstract
AbstractMRI is a popular imaging method for examining brain tumours. The ability to precisely segment tumours from MRI is absolutely essential for medical diagnostics and surgical planning. Manual tumour segmentation might be unrealistic for more comprehensive studies. Deep learning is the most widely used technique in medical diagnosis. For effective tumour dissection from brain MRI, this paper proposed a novel combination of FLAME and EHO Algorithm. FLAME is a type of clustering method that groups the most similar pixels in to a single cluster. EHO algorithm is one of the nature‐inspired metaheuristic optimization algorithms based on the social herding behaviour of elephants and swimming search methods. The proposed methodology's efficiency is validated through testing on various BraTS challenge datasets. The average computational time, mean squared error, peak signal to noise ratio, tanimoto coefficient, and dice score ‐ obtained are 23.3775 s, 0.213, 54.9669 dB, 54.6148%, and 84.053%, respectively.
Subject
Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials
Cited by
1 articles.
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