PSO Based MR Image Segmentation for Brain Tumor Detection

12 pages•Published: August 6, 2024

Jaspin K, Lakshmi Navya R and Lakshana A

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 analyses 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

In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 1-12.

Links:	https://easychair.org/publications/paper/2XLh
	https://doi.org/10.29007/zm9b

BibTeX entry

@inproceedings{ICSSIT2024:PSO_Based_MR_Image,
  author    = {Jaspin K and Lakshmi Navya R and Lakshana A},
  title     = {PSO Based MR Image Segmentation for Brain Tumor Detection},
  booktitle = {Proceedings of 6th International Conference on Smart Systems and Inventive Technology},
  editor    = {Rajakumar G},
  series    = {Kalpa Publications in Computing},
  volume    = {19},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/2XLh},
  doi       = {10.29007/zm9b},
  pages     = {1-12},
  year      = {2024}}

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