A Framework for Brain Tumor Image Analysis using Convolution with RELU

Authors

  • Pallavi Hallappanavar Basavaraja Associate Professor, Department of Information Science & Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore-560082, India
  • Nandeeswar Sampigehalli Basavaraju Professor,Department of Computer Science & Engineering - AI&ML, AMC Engineering College, Bangalore -560083, India
  • Pooja Nayak Associate Professor,Department of Information Science &Engineering Dayananda Sagar Academy of Technology and Management, Bangalore-560082, India
  • Anusha Preetham Associate Professor, Department of AI&ML B. N. M. Institute of technology, Bangalore, India
  • Ramya R. S. Associate professor, Department of Computer Science & Engineering, Dayananda Sagar College of Engineering , Kumaraswamy layout, Bangalore ,India
  • Shravya S. Assistant Professor, Department of Computer Science & Engineering City Engineering College, Bangalore

Keywords:

Brain tumor, BraTS 2020, Convolutional neural network, Convolution RELU, Magnetic resonance imaging (MRI)

Abstract

The most vital step in determining abnormal life-threatening tissues and creating an effective treatment plan for patients’ recovery, is classifying a brain tumor. There are several different medical imaging modalities available to detect abnormal disorders in the brain. Due to its superior image quality and lack of ionizing radiation, magnetic resonance imaging (MRI) is widely employed in medical imaging. Segmentation, detection, and classification are known to be crucial phases in a digital imaging pathology lab for MRI brain tumor region analysis. In this study for the analysis and classification of medical images, a convolution+RELU algorithm is implemented, which is a combination of convolutional and RELU optimization approaches. This paper employs a robust and efficient convolution+RELU method utilizing the BraTS 2020 dataset. This approach significantly reduces the segmentation time compared to other optimization methods. Moreover, it achieves impressive performance metrics, including a precision of 99.8%, recall of 99%, and an f-measure of 99.3%. Convolutional neural networks (CNN) that use the convolution+RELU activation function effectively increase the learning speed and tumor analysis performance. The implemented convolution+RELU model attained 99.8% accuracy in the experimental phase, which is higher than the existing techniques.

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Published

24.03.2024

How to Cite

Basavaraja, P. H. ., Basavaraju, N. S. ., Nayak, P. ., Preetham, A. ., R. S., R. ., & S., S. . (2024). A Framework for Brain Tumor Image Analysis using Convolution with RELU. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 312–321. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5254

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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