Image Fusion of MRI and CT Scan for Brain Tumor Detection Using VGG- 19

Authors

  • Rashmi Ashtagi Computer Department, Vishwakarma Institute of Technology Pune.
  • Swati Jadhav Department of Computer engineering Vishwakarma Institute of Technology Pune.
  • Abbas Madhavaswala Computer Department, Vishwakarma Institute of Technology Pune.
  • Ripul Handoo Computer Department, Vishwakarma Institute of Technology Pune.
  • Devanshu Rathi Computer Department, Vishwakarma Institute of Technology Pune.
  • Rishi Purohit Computer Department, Vishwakarma Institute of Technology Pune

Keywords:

Brain tumor detection, MRI, CT scan, Wavelet-based fusion, VGG-19 architecture, image analysis

Abstract

Brain tumor (BT) detection is crucial for patient outcomes, and bio-imaging techniques like Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans play a vital role in clinical assessment. However, manual analysis of these images is time-consuming and requires expertise. To address this, we propose an image fusion model that combines MRI and CT images using Wavelet-based fusion and leverages the VGG-19 architecture for improved accuracy. Image fusion combines modalities, enhancing their strengths while mitigating weaknesses. Our method employs the Wavelet fusion technique, decomposing images into frequency bands. The low-frequency LL band holds key structural information. The VGG-19 network, with its convolutional and pooling layers, is used to merge LL bands, reconstructing fused images. We conduct evaluations on brain MRI and CT images, employing preprocessing, feature extraction, and fusion stages. Our approach not only reduces the doctor's workload and analysis time but also enhances tumor detection accuracy. Automation of image analysis and early, accurate tumor identification lead to better patient care.

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Published

07.01.2024

How to Cite

Ashtagi, R. ., Jadhav, S. ., Madhavaswala, A. ., Handoo, R. ., Rathi, D. ., & Purohit, R. . (2024). Image Fusion of MRI and CT Scan for Brain Tumor Detection Using VGG- 19. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 369–377. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4386

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

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