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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM38804

Review on: Brain Disease Detection with the Help of MRI Report Using AI

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM38804

Volume:

8 |

11 |

Nov 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PHRA

Prof. H. R. Agashe

SG

Saurabh Gite

KH

Kunal Hande

AT

Aditya Tile

SW

Sakshi Warungase

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Abstract

The Brain Disease Detection Using MRI and AI project aims to develop an intelligent system that leverages advanced machine learning and deep learning algorithms to detect brain diseases from MRI scans. Brain disorders like tumor, stroke, and migraine are difficult to diagnose at early stages due to subtle patterns in MRI data, which can lead to delayed treatment. This project integrates a robust AI model trained on a comprehensive dataset of MRI scans to automate the diagnostic process, improving speed, accuracy, and consistency in detecting various brain diseases. The system consists of three primary modules: a Training Module for developing and optimizing the AI model, a Web Module for clinicians to interact with the system, and a Disease Detection Module that applies the trained AI model to analyze MRI scans and deliver diagnostic results. This automated approach provides a valuable tool for early diagnosis and timely medical interventions, ultimately improving patient outcomes.

KEYWORDS

Deep Learning Healthcare Technology Machine Learning (ML) Artificial Intelligence(AI) Automated Diagnosis and more.

Cite This Article

Prof. H. R. Agashe, Saurabh Gite, Kunal Hande, Aditya Tile, Sakshi Warungase, "Review on: Brain Disease Detection with the Help of MRI Report Using AI," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 11, Nov 2024.

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Publication Date

11/15/2024

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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2

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5

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The authors declare no conflict of interest related to this research work.

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