IJSDR
IJSDR
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

Issue: March 2024

Volume 9 | Issue 3

Impact factor: 8.15

Click Here For more Info

Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter

Copyright Infringement Claims
Indexing Partner
Published Paper Details
Paper Title: Approaching Analysis of MRI brain Cancer Classification using Amalgam Classifier (SVM-KNN)
Authors Name: Miss Geetanjali Somnath Gujarathi , Mr. Umesh Bhimrao Pagare , Mr. Atul Sheshrao Dadhe
Unique Id: IJSDR2204036
Published In: Volume 7 Issue 4, April-2022
Abstract: This research paper proposes a intellectual collection system to distinguish typical and strange MRI mind picture. MRI is an crucial technique used for brain tumor detection and judgment. Study of medical MRI images by the radiologist is very difficult and time irresistible task and correctness depending upon their experience. To overcome this problem, the automatic computer aided system becomes very enforced. The proposed paper presents an automatic computer aided system for classification of malignant and benign tumor from the brain MRI. The texture features are extracted from MRI by using the highly accurate Gray Level Co occurrence Matrix (GLCM) technique. The brain tumors are classified into malignant and benign using SVM and KNN classifiers. The proposed system gives an accuracy of 88.39% for SVM and 69.56% for KNN. To maintain a strategic distance from the human mistake, a computerized perceptive description framework is projected which provides food the requirement for characterization of picture. One of the real reasons for death among individuals is Brain tumor. The odds of survival can be expanded in the event that the tumor is identified effectively at its initial stage. Attractive reverberation imaging (MRI) strategy is utilized for the investigation of the human mind. In this investigation work, grouping methods in view of Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) are proposed and connected to mind picture arrangement. In this research paper we explore the hybrid classifier i.e. combination of two classifiers (SVM and KNN) so that the accuracy of the classifier will gets more. In this paper highlight extraction from MRI Images will be completed by dim scale, symmetrical and composition highlights. The primary target of this paper is to give a superb result (i.e. higher correctness rate what’s more, lower howler rate) of MRI cerebrum disease grouping utilizing SVM and KNN
Keywords: MRI, KNN, SVM, Brain Cancer, Skull Masking, Brain Cancer
Cite Article: "Approaching Analysis of MRI brain Cancer Classification using Amalgam Classifier (SVM-KNN) ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 4, page no.197 - 201, April-2022, Available :http://www.ijsdr.org/papers/IJSDR2204036.pdf
Downloads: 000336257
Publication Details: Published Paper ID: IJSDR2204036
Registration ID:200242
Published In: Volume 7 Issue 4, April-2022
DOI (Digital Object Identifier):
Page No: 197 - 201
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

Click Here to Download This Article

Article Preview

Click here for Article Preview







Major Indexing from www.ijsdr.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

Track Paper
Important Links
Conference Proposal
ISSN
DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related
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
Social Media
IJSDR

Indexing Partner