MRI Brain Tumor Classification and Extraction using Deep Learning-Based Decision Level Image Fusion Technique

Vasanthi Ravindran; Kalaiselvi Thiruvenkadam  lowast; Anitha Thiyagarajan

doi:10.17485/IJST/v17i27.1138

Article

MRI Brain Tumor Classification and Extraction using Deep Learning-Based Decision Level Image Fusion Technique

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i27.1138

Year: 2024, Volume: 17, Issue: 27, Pages: 2848-2857

Original Article

MRI Brain Tumor Classification and Extraction using Deep Learning-Based Decision Level Image Fusion Technique

Vasanthi Ravindran¹, Kalaiselvi Thiruvenkadam^2∗, Anitha Thiyagarajan³

¹Research Scholar, Department of Computer Science and Applications,, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, 624302, Tamil Nadu, India
²Assistant Professor, Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University),, Gandhigram, 624302, Tamil Nadu, India
³Assistant Professor, School of Computational Intelligence, Joy University, Raja Nagar, Alaganeri, Near Kanyakumari, College Rd, Vaddakkankulam, 627116, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:07 April 2024, Accepted Date:17 June 2024, Published Date:19 July 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The proposed work emphasizes the tumor region extracted from the multimodal MRI brain scan by deep learning-based decision-level image fusion technique. Methods: Convolutional Neural Network (CNN) architectures such as AlexNet, ResNet50, and VGG16 perform brain tumor classification with multimodal MRI images Flair, T2, and T1c respectively. Flair images are fed to the AlexNet architecture, T2 images are fed to the ResNet50 architecture, and T1c images are fed to the VGG16 architecture to classify brain tumor images. The classification results from these architectures are fused together to perform the decision on the given inputs. If the inputs come under the decision of the tumor affected then the tumor portion will be extracted using the fusion of three images as a post-processing operation. Findings: The experiments are done using BraTS datasets an open-access brain tumor image analysis research repository. The three CNN architectures' performance is measured by accuracy and gives 0.87 for AlexNet, 0.91 for ResNet50, and 0.99 for VGG16. The extracted tumor region from the fused output image is compared with the ground truth image by metrics such as SSIM with 0.93, DC 0.96, and PSNR with 66.57. Better results are received for the proposed work in the evaluation analysis than the existing works. Novelty: Decision level image fusion limitedly experimented with Deep Learning techniques in state-of-art methods. In this proposed method, the decisions made based on the classification result of three CNN architectures.

Keywords: MRI, Brain tumor, Deep Learning, Convolutional Neural Network Architectures, Image Fusion, AlexNet, ResNet50, VGG16

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Copyright

© 2024 Ravindran et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)