World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , Scope Database , Academia , 

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 : 7.029

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

  • ICV

    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level

  • New Issue Published

    Its Our pleasure to inform you that, WJERT March 2024 Issue has been Published, Kindly check it on

  • WJERT New Impact Factor

    WJERT Impact Factor has been Increased to 7.029 for Year 2024.


    March 2024 Issue has been successfully launched on 1 March 2024.




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


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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