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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

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Paper Title:	Brain Tumor Detection using Hybrid Machine Learning Models
Authors Name:	Isha Dave , Shreyas Tuttagunta
Unique Id:	IJSDR2307126
Published In:	Volume 8 Issue 7, July-2023
Abstract:	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.
Keywords:	Brain tumor detection, machine learning, deep learning, medical imaging, MRI scans, convolutional neural networks.
Cite Article:	"Brain Tumor Detection using Hybrid Machine Learning Models", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 7, page no.856 - 859, July-2023, Available :http://www.ijsdr.org/papers/IJSDR2307126.pdf
Downloads:	000338536
Publication Details:	Published Paper ID: IJSDR2307126 Registration ID:207826 Published In: Volume 8 Issue 7, July-2023 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.36094 Page No: 856 - 859 Publisher: IJSDR \| www.ijsdr.org ISSN Number: 2455-2631

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