INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Brain tumors represent a significant health concern worldwide, necessitating accurate and timely diagnosis for effective treatment planning. With advancements in medical imaging technology, there has been a surge in the volume and complexity of imaging data, demanding robust computational methods for accurate detection and grading of brain tumors. In this context, deep learning techniques have shown promise due to their ability to automatically learn hierarchical features from imaging data. This project proposes a novel Multi-Model Fusion Deep Learning (MMFDL) approach for the detection and grading of brain tumors using multimodal imaging data, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and positron emission tomography (PET). The proposed MMFDL model leverages the complementary information provided by these modalities to enhance the accuracy and reliability of tumor detection and grading.
Keywords:
Brain Tumor, MRI, Machine Learning, Tumor, CNN
Cite Article:
"MultiModal Fusion Deep Learning Model For Brain Tumor Classification And Grading", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.808 - 813, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404113.pdf
Downloads:
000338172
Publication Details:
Published Paper ID: IJSDR2404113
Registration ID:210871
Published In: Volume 9 Issue 4, April-2024
DOI (Digital Object Identifier):
Page No: 808 - 813
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
Facebook Twitter Instagram LinkedIn