Glioma detection using EHO based FLAME clustering in MR brain images

• Published in: International journal of imaging systems and technology 2024-01, Vol.34 (1), p.n/a
• Main Authors: Karun, Baiju, Prasath, T. Arun, Rajasekaran, M. Pallikonda, Makreri, Rakhee
• Format: Article
• Language: English
• Subjects: Algorithms; Brain; BraTS dataset; Clustering; Computing time; deep learning (DL); elephant herding optimization (EHO); fuzzy clustering by local approximation of memberships (FLAME); Heuristic methods; Magnetic resonance imaging; Signal to noise ratio; Tumors; tumour segmentation
• ISSN: 0899-9457; 1098-1098
• DOI: 10.1002/ima.22937

MRI 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.