Research Article
Intra Fusion Based CNN Technique for MRI Multimodal Brain Tumor Classification and Segmentation
@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
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.
