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World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

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Indexing

Abstract

AUTOMATED BRAIN TUMOUR DETECTION BY USING DEEP LEARNING FROM MAGNETIC RESONANCE IMAGING

*Moirangmayum Bebi Devi, Dr. Thiyam Ibungomacha Singh and Leishangthem Hemapati Devi

ABSTRACT

The emerging field in many medical diagnostic applications is automated flaw detection in medical imaging. Automated brain tumour diagnosis in MRI is essential because it offers details about aberrant tissues needed for treatment planning. Human inspection is the standard procedure for flaw detection in magnetic resonance brain pictures. Due to the volume of data, this strategy is impracticable. Therefore, reliable and automatic classification systems are necessary to reduce the number of human deaths. Therefore, automated tumour identification techniques are being developed in order to reduce radiologists' time and achieve a proven level of accuracy. Due to the intricacy and variety of tumours, MRI brain tumour detection is a challenging undertaking. The main goal of this study is to use MRI images to locate and diagnose a brain tumour. We suggest using deep learning methods to get around the shortcomings of conventional classifiers. Through MRI, it is possible to effectively find cancer cells in the brain using deep learning and image classification. This paper's main goal is to use MRI images to locate and diagnose a brain tumour.

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