Abstract
Brain tumors are abnormal cells that grow within the brain, some of which can cause malignant growth. The standard method for distinguishing between cancer and the mind is magnetic resonance imaging (MRI). Magnetic resonance imaging data enables the identification of the development of strange tissues in the brain. In a variety of review papers, the localization of mind cancer is complemented by the application of machine learning and in-depth learning calculations. After applying these calculations to MRI images, brain tumor prediction is abnormally fast and higher accuracy helps treat patients. The predictions also allow radiologists to make quick choices. In this paper, a combination of artificial neural networks (ANN) and convolutional neural networks (CNN) is proposed and applied to identify the presence of brain tumors.
Supported by Integral University, Lucknow (MCN: IU/R&D/2021-MCN0001229).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hashemzehi R, Mahdavi SJS, Kheirabadi M, Kamel SR (2020) Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE. Elsevier B.V. on behalf of Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences Online Publication
Cancer data https://www.cancer.net/cancer-types/brain-tumor/statistics. Last accessed 10 Aug 21 https://braintumor.org/brain-tumor-information/treatment-options/clinical-trials/ (Feb2020). Last accessed 10 Aug 21
Google Sites https://en.wikipedia.org/wiki/Convolutional-neural-network Last accessed 10 Aug 21 https://en.wikipedia.org/wiki/Artificial-neural-network Last accessed 12 Aug 21 https://www.geeksforgeeks.org/introduction-convolution-neural-network/ Last accessed 11 Aug 21 https://en.wikipedia.org/wiki/Brain-tumor Last accessed 9 Aug 21
Nalbalwar R, Majhi U, Patil R, Gonge S (2014) Detection of brain tumor by using ANN. Int J Res Advent Technol 2
Milletari F, Ahmadi SA, Kroll C, Plate A, Rozanski V, Maiostre J, Levin J, Dietrich O, Ertl-Wagner B, Bötzel K, Navab N (2016) Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Elsevier Inc., 164, 92-3-102
Özyurt F, Sert E, Avci E, Dogantekin E (2019) Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy. Elsevier Ltd., 147
Amin J, Sharif M, Raza M, Yasmin M (2018) Detection of brain tumor based on features fusion and machine learning. J Amb Intell Humanized Comput Online Publ
George DN, Jehlol HB, Oleiwi ASA (2015) Brain tumor detection using shape features and machine learning algorithms. Int J Sci Eng Res 6(12):454–459
Sobhangi K, Avinash S, Sabyasachi K, Satyabrata C, Jong-Seong A, Hee-Cheol S (2020) A CNN based Approach for the detection of brain tumor using MRI Scans. Test Eng Manage 83:16580–16586
Sharma K, Kaur A, Gujral S (2014) Brain tumor detection based on machine learning algorithms. Int J Comput Appl 103:7–11
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jahan, R., Tripathi, M.M. (2022). Detection of Brain Tumors in MRI Images Through Deep Learning. In: Kaiser, M.S., Bandyopadhyay, A., Ray, K., Singh, R., Nagar, V. (eds) Proceedings of Trends in Electronics and Health Informatics. Lecture Notes in Networks and Systems, vol 376. Springer, Singapore. https://doi.org/10.1007/978-981-16-8826-3_11
Download citation
DOI: https://doi.org/10.1007/978-981-16-8826-3_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8825-6
Online ISBN: 978-981-16-8826-3
eBook Packages: EngineeringEngineering (R0)