IJRTI
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.14

Issue per Year : 12

Volume Published : 10

Issue Published : 115

Article Submitted : 19455

Article Published : 8041

Total Authors : 21252

Total Reviewer : 769

Total Countries : 144

Indexing Partner

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: SEMANTIC SEGMENTATION BASED CNN FOR TUMOR DETECTION
Authors Name: V.Parvathi , P.Vivekanand , G.Sahitya , S.Vidya Sagar
Download E-Certificate: Download
Author Reg. ID:
IJRTI_187377
Published Paper Id: IJRTI2306121
Published In: Volume 8 Issue 6, June-2023
DOI:
Abstract: A tumor is a growth of cells in the organs or near it. Tumors can happen in the any tissue. Nearby locations include nerves, the pituitary gland, the pineal gland, and the membranes that cover the surface of the brain. Although MRI images are a popular imaging method for evaluating these tumors, the volume of data it generates makes it difficult to manually segment the images in a reasonable amount of time, which restricts the use of precise quantitative assessments in clinical settings. The enormous spatial and structural heterogeneity among tumors makes automatic segmentation a difficult task, hence dependable and automatic segmentation methods are needed. In our project we developed deep learning models based on convolutional neural network and Edge Detection algorithms to perform the automated semantic image segmentation of the MRI images of the tumor. We explored the current state of the CNNs architecture and evaluated them on the BraTS dataset. Different regularization methods and hyperparameters are tested and optimized through a series of experiments.
Keywords: Tumor, MRI images, Edge Detection, Deep learning, CNN.
Cite Article: "SEMANTIC SEGMENTATION BASED CNN FOR TUMOR DETECTION", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 6, page no.796 - 802, June-2023, Available :http://www.ijrti.org/papers/IJRTI2306121.pdf
Downloads: 000205225
ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2306121
Registration ID:187377
Published In: Volume 8 Issue 6, June-2023
DOI (Digital Object Identifier):
Page No: 796 - 802
Country: vizianagaram, Andhara pradesh, India
Research Area: Computer Engineering 
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2306121
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2306121
Share Article:

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from www.ijrti.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

ISSN Details

ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI.ONE
How to Get DOI?

Conference

Open Access License Policy

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

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Join RMS/Earn 300

IJRTI

WhatsApp
Click Here

Indexing Partner