Brain Tumor Detection in MRI Images using Convolutional Neural Network Technique

International Journal of Electrical and Electronics Engineering

© 2022 by SSRG - IJEEE Journal

Volume 9 Issue 12

Year of Publication : 2022

Authors : R. Tamilaruvi, R. Vijayalakshmi, M. Ganthimathi, R. Surendiran, M. Thangamani, S. Satheesh

10.14445/23488379/IJEEE-V9I12P118

How to Cite?

R. Tamilaruvi, R. Vijayalakshmi, M. Ganthimathi, R. Surendiran, M. Thangamani, S. Satheesh, "Brain Tumor Detection in MRI Images using Convolutional Neural Network Technique," SSRG International Journal of Electrical and Electronics Engineering, vol. 9,  no. 12, pp. 198-208, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I12P118

Abstract:

A brain tumor is a type of cancer that is difficult to detect. As a result, it is more important for care to evaluate nodules swiftly and appropriately for both men and women. As a result, numerous approaches for detecting brain tumors in their early stages have been developed. A comparative comparison of multiple strategies based on machine learning and deep learning for brain tumor identification has been offered in this procedure. There have been far too many approaches for diagnosing brain tumors developed in recent years, the majority of which rely on MRI images. In addition, several classifier methods are used in conjunction with threshold segmentation algorithms to locate tumors using picture recognition. MRI gray scale images have been discovered to be more suitable for obtaining accurate results because of this method. As a result, most MRI scan images are used to detect tumors in the brain. Furthermore, the findings obtained from approaches based on machine learning and deep learning techniques were more accurate than those obtained from methods based on traditional deep learning techniques. The deep learning method was proposed using the Convolutional neural network to predict the outcome with high accuracy.

Keywords:

Brain tumor, Convolutional neural network (CNN), Deep learning, MRI(Magnetic Resonance Imaging), Threshold segmentation.

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