|
|||||||||||||||
|
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (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) |
||||||||||||||
Issue: May 2024
Volume 9 | Issue 5
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.76
Issue per Year : 12
Volume Published : 9
Issue Published : 96
Article Submitted :
Article Published :
Total Authors :
Total Reviewer :
Total Countries :
Indexing Partner
Join RMS/Earn 300
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | BRAIN TUMOR DETECTION USING U-NET++ SEGMENTATION TECHNIQUE |
| Authors Name: | Jayashree Shedbalkar , Dr. Kprabhushetty , Prof. A S Inchal |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_197687
|
| Published Paper Id: | IJNRD2307275 |
| Published In: | Volume 8 Issue 7, July-2023 |
| DOI: | |
| Abstract: | Segmenting Brain Tumor images is crucial for computer-assisted diagnosis. The key to effective segmentation is for the model to be able to both see the overall picture and the minute details, or to learn image characteristics that contain a lot of contexts while maintaining high spatial resolutions. The most popular techniques, U-Net and its variations, extract and fuse multi-scale information in order to reach this aim. The fused features performance is nonetheless constrained by their tiny effective receptive fields and emphasis on local visual signals. In this work, we use a variety of machine learning techniques to forecast the survival rate. To conduct segmentation, we use a 3D UNet++ architecture and combine channel and spatial attention with the decoder network. To forecast the length of each patient's survival, we extract certain unique radiomic parameters based on the geometry, position, and shape of the segmented tumor and integrate them with clinical data. To demonstrate the impact of each attribute on the prediction of overall survival (OS), we also conduct comprehensive studies. According to the experimental findings, the most important factors to determine the OS are clinical characteristics like age and radionics properties like the histogram, location, and shape of the necrosis area. we offer Segtran, a different segmentation framework built on transformers and UNet++, which even at high feature resolutions have an infinite effective receptive field. Segtran's central component is a new squeeze-and-expansion UNet++, in which an expansion block learns a variety of representations while a squeezed attention block regulates the self-attention of transformers. We also provide a brand-new positional encoding approach for transformers that imposes an image continuity inductive bias. |
| Keywords: | Brain tumor segmentation, Glioma, Regression, Attention, |
| Cite Article: | "BRAIN TUMOR DETECTION USING U-NET++ SEGMENTATION TECHNIQUE", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 7, page no.c761-c770, July-2023, Available :http://www.ijnrd.org/papers/IJNRD2307275.pdf |
| Downloads: | 000118752 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2307275 Registration ID: 197687 Published In: Volume 8 Issue 7, July-2023 DOI (Digital Object Identifier): Page No: c761-c770 Country: Haliyal, karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2307275 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2307275 |
| Share Article: |
|
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijnrd.org
| Semantic Scholar | Microsaoft Academic | ORCID | Zenodo |
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | PUBLON |
| DRJI | SSRN | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Social Media
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Join RMS/Earn 300
| Copyright © 2024 - All Rights Reserved - IJNRD |
Facebook Twitter Instagram LinkedIn