Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India

Research Article

Intra Fusion Based CNN Technique for MRI Multimodal Brain Tumor Classification and Segmentation

Download56 downloads
  • @INPROCEEDINGS{10.4108/eai.23-11-2023.2343286,
        author={Vasanthi Ravindran and Kalaiselvi Thiruvengadam and Anitha Thiyagarajan},
        title={Intra Fusion Based CNN Technique for MRI Multimodal Brain Tumor Classification and Segmentation},
        proceedings={Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India},
        publisher={EAI},
        proceedings_a={IACIDS},
        year={2024},
        month={3},
        keywords={deep learning convolutional neural network image fusion piece - wise linear transformation mri brain tumor substructure segmentation morphological operation},
        doi={10.4108/eai.23-11-2023.2343286}
    }
    
  • Vasanthi Ravindran
    Kalaiselvi Thiruvengadam
    Anitha Thiyagarajan
    Year: 2024
    Intra Fusion Based CNN Technique for MRI Multimodal Brain Tumor Classification and Segmentation
    IACIDS
    EAI
    DOI: 10.4108/eai.23-11-2023.2343286
Vasanthi Ravindran1,*, Kalaiselvi Thiruvengadam1, Anitha Thiyagarajan1
  • 1: Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India
*Contact email: vasanthics2010@gmail.com

Abstract

The proposed work segment the tumor portion with substructure from MRI Multimodal brain tumor images using image fusion techniques. The preprocessing work is done by using Piece-wise linear transformation, to enhance the tumor region. The proposed work classify the brain tumor image as tumor or non-tumor by convolutional neural network (CNN) model, then extracts the whole tumor portion by largest connected component (LCC) and finally segments the substructures. The segmented substructure of tumor portion is validated with ground truth in qualitative and quantitative analysis. The experiments are done using BraTS datasets and performance metrics such as structural similarity index measure (SSIM), accuracy, dice coefficient (DC), and peak signal to noise ratio (PSNR). This metrics are used to validate the shape of the tumor portion. The metrics gives better results for the proposed work.