PSO Based MR Image Segmentation for Brain Tumor Detection

EasyChair Preprint 12987

Abstract

Brain tumor segmentation is an essential step that is important for the diagnosis and treatment planning in healthcare. Brain MRI images are preprocessed in accordance with the suggested approach before data is gathered and ready for further analysis. The suggested study introduces a new strategy that uses the bio-inspired Particle Swarm Optimization (PSO) algorithm to segment brain tumor images. To improve accuracy and dependability, the segmentation model's parameters can be adjusted. Standard measures like Accuracy, Precision, Sensitivity, Jaccard index, Dice Coefficient, Specificity are used in performance evaluation to measure the effectiveness of the suggested PSO-based segmentation approach. The overall accuracy of the suggested method is 98.5%. Subsequent performance analys es yield better results of 91.95%, 87.01%, 92.36%, 90%, and 99.7% for Dice Score Coefficient, Jaccard Index, Precision,Sensitivity, and Specificity, respectively. Therefore,this method can be a useful tool for radiologists, supporting them in diagnosis of tumor in brain.

Keyphrases: Brain Tumor, Magnetic Resonance Images, Particle Swarm Optimization, Swarm Intelligence

@booklet{EasyChair:12987,
  author    = {K Jaspin and R Lakshmi Navya and A Lakshana},
  title     = {PSO Based MR Image Segmentation for Brain Tumor Detection},
  howpublished = {EasyChair Preprint 12987},
  year      = {EasyChair, 2024}}