 |
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)
|
Issue: March 2026
Volume 11 | Issue 3
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 : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 22135
Article Published : 8686
Total Authors : 22851
Total Reviewer : 819
Total Countries : 160
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | DEEP LEARNING APPROACHES FOR BRAIN TUMOR DETECTION IN MRI SCANS |
| Authors Name: | P KRISHNA TEJA |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_189464
|
| Published Paper Id: | IJRTI2403088 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | : This study investigates the effectiveness of several machine learning methods in identifying brain cancers from MRI images, with a specific emphasis on deep neural networks. The study evaluates the efficacy of Gradient Boosting, Neural Networks, XGBoost, and Convolutional Neural Networks (CNNs) in accurately categorizing MRI images to detect the presence of tumours. The results demonstrate that both conventional machine learning models and deep learning models attain exceptional accuracy. Specifically, the Gradient Boosting and XGBoost classifiers display accuracies of 98.14% and 98.27%, respectively. The Neural Network classifier obtains an accuracy of 85.52%, while the CNN model has a test accuracy of 91.76%. The findings indicate that deep learning techniques, namely Convolutional Neural Networks (CNNs), have great potential in reliably detecting brain cancers from MRI data. This research enhances the current endeavours in medical imaging analysis by providing valuable knowledge on the use of machine learning for the prompt and precise detection of brain illnesses. |
| Keywords: | |
| Cite Article: | "DEEP LEARNING APPROACHES FOR BRAIN TUMOR DETECTION IN MRI SCANS", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.9, Issue 3, page no.642 - 650, March-2024, Available :http://www.ijrti.org/papers/IJRTI2403088.pdf |
| Downloads: | 000205208 |
| 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: IJRTI2403088
Registration ID:189464
Published In: Volume 9 Issue 3, March-2024
DOI (Digital Object Identifier):
Page No: 642 - 650 Country: Guntur, AndhraPradesh, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2403088 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2403088 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
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
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||