Brain Tumor Detection using Hybrid Machine Learning Models
Isha Dave
, Shreyas Tuttagunta
Brain tumor detection, machine learning, deep learning, medical imaging, MRI scans, convolutional neural networks.
Brain tumor detection holds a critical role in swiftly diagnosing and planning treatments for improved patient outcomes. Conventional approaches to detecting brain tumors rely on radiological imaging methods and manual analysis, which can be time-consuming and prone to human error. However, in recent years, the advent of machine learning models has revolutionized this process by offering automated brain tumor detection. This promising advancement not only enhances accuracy but also boosts efficiency levels significantly. This research paper delves into an examination of utilizing machine learning models for the purpose of brain tumor detection. The primary aim of this study is to investigate and determine the efficacy of various machine learning algorithms in accurately identifying brain tumors from medical imaging data, specifically MRI scans.
"Brain Tumor Detection using Hybrid Machine Learning Models", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 7, page no.856 - 859, July-2023, Available :https://ijsdr.org/papers/IJSDR2307126.pdf
Volume 8
Issue 7,
July-2023
Pages : 856 - 859
Paper Reg. ID: IJSDR_207826
Published Paper Id: IJSDR2307126
Downloads: 000347257
Research Area: Information Technology
Country: Mumbai, Maharashtra, India
DOI: http://doi.one/10.1729/Journal.36094
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
