Brain Tumor Segmentation and Detection using EfficientNetB3 Model for MRI Medical Images

Authors

  • Upendra Singh Assistant Professor , Department of Information Technology, Shri G. S. Institute of Technology and Science, Indore, India
  • Rini Saxena Assistant Professor,Department of Computer Science & Engineering, Chandigarh Engineering College Jhanjeri, Mohali, Punjab, India
  • Tarun Sharma Assistant Professor,Department of Computer Science, Softvision College, Indore, India
  • Sambit Ray Department of Computer Science, BITS, India
  • Preeti Mishra Assistant Professor , Technology Department, Regenesys Business School, Navi Mumbai, India
  • Puja Gupta Assistant Professor , Department of Information Technology, Shri G. S. Institute of Technology and Science, Indore, India
  • Mukul Shukla Associate professor,Department of Information Technology, Shri G. S. Institute of Technology and Science, Indore, India

Keywords:

Machine Learning, Brain tumors, X-ray images, Magnetic Resonance Imaging, Computed Tomography, EfficientNetB3, Radial Basis Function

Abstract

Brain tumors, identified by their abnormal cell proliferation within the brain, have historically been diagnosed and delineated using Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. Recently, the use of X-rays, known for their speed and greater accessibility, for the detection of brain tumors has sparked interest in the medical community. This study aims to assess the efficacy of various computational techniques in the identification and delineation of brain tumors from MRI images. The investigation covered traditional models such as the Radial Basis Function (RBF), Linear, and Polygonal kernels, which demonstrated accuracy rates between 65.74% and 86.24% across two separate test images. Additionally, the research delved into the capabilities of the EfficientNetB3 model, distinguished by its deep learning prowess and innovative compound scaling approach. The findings revealed that the EfficientNetB3 model surpassed the conventional methods, achieving accuracy levels of 93.49% and 94.73% on the two test images, respectively. These results underscore the substantial promise of the EfficientNetB3 model in enhancing the precision of medical imaging analyses, representing a significant leap forward in the diagnostic and treatment planning processes for brain tumor patients.

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Published

24.03.2024

How to Cite

Singh, U. ., Saxena, R. ., Sharma, T. ., Ray, S. ., Mishra, P. ., Gupta, P. ., & Shukla, M. . (2024). Brain Tumor Segmentation and Detection using EfficientNetB3 Model for MRI Medical Images. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 296–307. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5066

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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