Brain Tumor Detection Using Deep Learning

Brain Tumor Detection Using Deep Learning

Registration ID: IJNRD_213599

Published ID: IJNRD2402145

DOI: Click Here to Get

Ch. Rajendraprasad , Sheik Iqlaq Ahmad , Seeloju Divya , Chiluka Thrini

Convolution Neural Network, Machine Learning, Brain tumor, Algorithms.

Tumors are a prominent concern in modern healthcare, ranking as the second leading cause of cancer-related fatalities and posing a significant threat to countless patients. Swift, automated, efficient, and dependable tumor detection methods are crucial, particularly when focusing on brain tumors. The precise identification of tumors is paramount to ensure timely treatment and protect patients from harm. This paper delves into a range of image processing techniques with the overarching goal of achieving this critical objective. By incorporating these techniques, healthcare professionals can devise accurate treatment plans, potentially saving numerous lives. Fundamentally, tumors represent abnormal clusters of cells that multiply uncontrollably. Brain tumors, in particular, follow a growth pattern that progressively deprives healthy brain cells and tissues of essential nutrients, leading to cognitive dysfunction. Currently, physicians heavily rely on manually scrutinizing MRI images of a patient's brain to locate and assess the extent of a brain tumor. However, this approach is susceptible to inaccuracies and consumes an excessive amount of time.In response to this challenge, we advocate for the integration of deep learning models, specifically Convolutional Neural Networks (CNNs), also known as Neural Networks (NNs), in conjunction with the VGG 16 (Visual Geometry Group) model using transfer learning. Our model's primary objective is to predict the presence or absence of a brain tumor within a given image. If a tumor is detected, the model yields a "yes" output; otherwise, it provides a "no" response. Leveraging the potential of deep learning and transfer learning, our approach presents a promising solution for efficient and accurate brain tumor detection.The performance of our model is assessed based on its ability to accurately differentiate between the presence and absence of a brain tumor in medical images, significantly contributing to the improvement of patient care and outcomes in the fight against this devastating disease.

Published Paper   E-Certificate

"Brain Tumor Detection Using Deep Learning", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 2, page no.b345-b349, February-2024, Available :https://ijnrd.org/papers/IJNRD2402145.pdf

Volume 9 Issue 2, February-2024

Pages : b345-b349

Paper Reg. ID: IJNRD_213599

Published Paper Id: IJNRD2402145

Downloads: 000121229

Research Area: Science & Technology

Country: Hyderabad, Telangana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2402145.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2402145

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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