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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References

Jain, R. "Perfusion CT imaging of brain tumors: an overview." American Journal of Neuroradiology 32, no. 9 (2011): 1570-1577

Alfonse, M. and Salem, A.B.M., 2016. An Automatic Classification of Brain Tumors through MRI Using Support Vector Machine. Egyptian Computer Science Journal, 40(3).

Saeed, S., & Abdullah, A. B. (2019, March). Investigation of a brain cancer with interfacing of 3-dimensional image processing. In 2019 International Conference on Information Science and Communication Technology (ICISCT) (pp. 1-6). IEEE.

Anil, Abhishek, Aditya Raj, H. Aravind Sarma, and NCRD PL. "Brain Tumor detection from brain MRI using Deep Learning." International Journal of Innovative Research in Applied Sciences and Engineering (IJIRASE) 3, no. 2 (2019): 458-465.

Ma, X., Hu, S., Liu, S., Fang, J., & Xu, S. (2019). Remote sensing image fusion based on sparse representation and guided filtering. Electronics, 8(3), 303.

Gao, J., Li, J., & Jiang, M. (2021). Hyperspectral and multispectral image fusion by deep neural network in a self-supervised manner. Remote Sensing, 13(16), 3226.

Sharma, Sarang, et al. "Deep Learning Model for Automatic Classification and Prediction of Brain Tumor." Journal of Sensors 2022 (2022).

Fu, J., Li, W., Ouyang, A., & He, B. (2021). Multimodal biomedical image fusion method via rolling guidance filter and deep convolutional neural networks. Optik, 237, 166726.

Shin, Hoo-Chang, et al. "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning." IEEE transactions on medical imaging 35.5 (2016): 1285-1298.

Banerjee, Sreeparna, Dipti Prasad Mukherjee, and D. Dutta Majumdar. "Point landmarks for registration of CT and MR images." Pattern Recognition Letters 16.10 (1995): 1033-1042.

Banerjee, Sreeparna, Dipti Prasad Mukherjee, and D. Dutta Majumdar. "Point landmarks for registration of CT and MR images." Pattern Recognition Letters 16.10 (1995): 1033-1042.

Liu, J., Li, Z., & He, J. (2020). A novel multimodal medical image fusion method based on convolutional neural network and edge-preserving filter. Multimedia Tools and Applications, 79(31), 22245-22260.

Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., ... & Do, S. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285-1298.

Sun, X., Wu, J., Yan, L., & Yin, B. (2019). Transfer learning for hyperspectral and multispectral image fusion: A comparative study. Information Fusion, 45, 1-15.

Zhang, J., Li, X., Chen, X., & Tao, D. (2019). Remote sensing image fusion via sparse representation and dictionary learning. Information Fusion, 45, 59-70.

Gupta, M., & Verma, R. (2021). A survey of image fusion using deep learning. Artificial Intelligence Review, 54(5), 3085-3137.

S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz and D. Terzopoulos, "Image Segmentation Using Deep Learning: A Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523-3542, 1 July 2022, doi: 10.1109/TPAMI.2021.3059968.

Beucher, S., & Lantuejoul, C. (1979). "Use of watersheds in contour detection". In Proceedings of the International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation (pp. 184-203).

Gonzalez, Rafael C., Richard E. Woods, and Barry R. Masters. “Digital Image Processing, Third Edition.” Journal of Biomedical Optics 14.2 (2009): 029901. Web.

Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), 1345-1359.

Sarang Sharma, Sheifali Gupta, Deepali Gupta, Abhinav Juneja, Harsh Khattar, Sapna Malik & Zelalem Kiros Bitsue (2022) Deep Learning Model for Automatic Classification and Predication of Brain Tumor.

Patil, Rashmi, and Sreepathi Bellary. "Transfer Learning Based System for Melanoma Type Detection." Revue d'Intelligence Artificielle 35.2 (2021).

Mane, Deepak, et al. "An Improved Transfer Learning Approach for Classification of Types of Cancer." Traitement du Signal 39.6 (2022): 2095.

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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://ijisae.org/index.php/IJISAE/article/view/4386

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

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