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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

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Impact Factor : 8.14

Issue per Year : 12

Volume Published : 8

Issue Published : 81

Article Submitted : 5865

Article Published : 3236

Total Authors : 8222

Total Reviewer : 541

Total Countries : 72

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Published Paper Details
Paper Title: Brain Tumor Detection Using Convolutional Neural Network
Authors Name: Vamsi Krishna Yalamanchili , M. Prameela
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IJRTI_184553
Published Paper Id: IJRTI2211032
Published In: Volume 7 Issue 11, November-2022
DOI:
Abstract: Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. In this paper, we proposed a method to extract brain tumor from 2D Magnetic Resonance brain Images (MRI) by Fuzzy C-Means clustering algorithm which was followed by traditional classifiers and convolutional neural network. The experimental study was carried on a real-time dataset with diverse tumor sizes, locations, shapes, and different image intensities. In traditional classifier part, we applied six traditional classifiers namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Logistic Regression, Naïve Bayes and Random Forest which was implemented in scikit-learn. Afterward, we moved on to Convolutional Neural Network (CNN) which is implemented using Keras and Tensorflow because it yields to a better performance than the traditional ones. In our work, CNN gained an accuracy of 97.87%, which is very compelling. The main aim of this paper is to distinguish between normal and abnormal pixels, based on texture based and statistical based features.
Keywords: CNN, FCM, Medical Image, segmentation, SVM
Cite Article: "Brain Tumor Detection Using Convolutional Neural Network", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 11, page no.201 - 206, November-2022, Available :http://www.ijrti.org/papers/IJRTI2211032.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2211032
Registration ID:184553
Published In: Volume 7 Issue 11, November-2022
DOI (Digital Object Identifier):
Page No: 201 - 206
Country: N.T.R, Andhra Pradesh, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2211032
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2211032
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

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