UGC Approved Journal no 63975(19)

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Volume 11 | Issue 10 | October 2024

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Volume 11 Issue 10
October-2024
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIRGN06019


Registration ID:
549130

Page Number

161-171

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Title

BRAIN TUMOR CLASSIFICATION WITH FULLY CONVOLUTIONAL NEURAL NETWORK: A DEEP LEARNING APPROACH

Abstract

Detection and Classification of a brain tumour is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumour region. However, it is a time taking process and requires expertise to test the MRI images, manually. Nowadays, the advancement of Computer-assisted Diagnosis (CAD), machine learning, and deep learning in specific allow the radiologist to more reliably identify brain tumours. The traditional machine learning methods used to tackle this problem require a handcrafted feature for classification purposes. Typically, the expertise of neurosurgical specialists is required for the precise analysis of MRI scans. Unfortunately, in many developing countries, a shortage of skilled medical professionals and limited awareness about brain tumours compound the difficulties associated with obtaining timely and accurate MRI results.Whereas deep learning methods can be designed in a way to not require any handcrafted feature extraction while achieving accurate classification results. this research introduces FCNN, a special type of deep learning model called a Fully Convolutional Neural Network (FCNN) designed to classify brain tumours into different categories. The FCNN architecture demonstrates impressive results, achieving a precision score of 95.85 percent and accuracy rates of 99.98 percent during training and 98.12 percent during testing. This accomplishment has the potential to significantly improve brain tumour diagnosis and classification, particularly in areas with limited access to medical resources and knowledge.

Key Words

Tumor,Convolutional Neural Network(CNN), Deep learning, Magnetic Reasoning Image(MRI), FCNN

Cite This Article

"BRAIN TUMOR CLASSIFICATION WITH FULLY CONVOLUTIONAL NEURAL NETWORK: A DEEP LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.161-171, October-2024, Available :http://www.jetir.org/papers/JETIRGN06019.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"BRAIN TUMOR CLASSIFICATION WITH FULLY CONVOLUTIONAL NEURAL NETWORK: A DEEP LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp161-171, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06019.pdf

Publication Details

Published Paper ID: JETIRGN06019
Registration ID: 549130
Published In: Volume 11 | Issue 10 | Year October-2024
DOI (Digital Object Identifier):
Page No: 161-171
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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