Image Segmentation Using Machine Learning for Multimodal Medical Images

Authors

  • Kajal Chheda Department of ISME, ATLAS SkillTech University, Mumbai, Maharashtra, India
  • Akhilendra Pratap Singh Maharishi University of Information Technology, Lucknow, India -226036
  • Raman Verma Chitkara University, Rajpura, Punjab, India
  • Sunil M. P. JAIN (Deemed-to-be University), Karnataka, India
  • Shweta Madaan Vivekananda Global University, Jaipur

Keywords:

Image segmentation, multimodal medical images, therapeutic applications, birds swarm optimized random forest (BSORF)

Abstract

A basic assignment in medical imaging, the segmentation of images is essential for many therapeutic applications. Because of the inherent difficulty and variability in various types of imaging, separating multimodal medical pictures presents major hurdles. The research suggests a novel image segmentation method called bird swarm optimized random forest (BSORF) for multimodal medical concepts. The BSO algorithm is used in the proposed approach to enhance the feature selection procedure and make it possible to identify the best particular characteristics in multimodal clinical images. The RF method, renowned for its efficiency when processing complex data and categorization assignments, is then used with these chosen characteristics as input. Numerous tests were run on multimodal medical data to assess our strategy's performance. The results show that the suggested method outperforms current approaches regarding accurate segmentation.

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Published

24.03.2024

How to Cite

Chheda, K. ., Pratap Singh, A., Verma, R. ., M. P., S. ., & Madaan, S. . (2024). Image Segmentation Using Machine Learning for Multimodal Medical Images. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 727–732. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5202

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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