research-article

Deep CNN for Brain Tumor Classification

Authors:

Mohamed AtriAuthors Info & Claims

Neural Processing Letters, Volume 53, Issue 1

Pages 671 - 700

https://doi.org/10.1007/s11063-020-10398-2

Published: 01 February 2021 Publication History

Abstract

Brain tumor represents one of the most fatal cancers around the world. It is common cancer in adults and children. It has the lowest survival rate and various types depending on their location, texture, and shape. The wrong classification of the tumor brain will lead to bad consequences. Consequently, identifying the correct type and grade of tumor in the early stages has an important role to choose a precise treatment plan. Examining the magnetic resonance imaging (MRI) images of the patient’s brain represents an effective technique to distinguish brain tumors. Due to the big amounts of data and the various brain tumor types, the manual technique becomes time-consuming and can lead to human errors. Therefore, an automated computer assisted diagnosis (CAD) system is required. The recent evolution in image classification techniques has shown great progress especially the deep convolution neural networks (CNNs) which have succeeded in this area. In this regard, we exploited CNN for the problem of brain tumor classification. We suggested a new model, which contains various layers in the aim to classify MRI brain tumor. The proposed model is experimentally evaluated on three datasets. Experimental results affirm that the suggested approach provides a convincing performance compared to existing methods.

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Cited By

Islam MTalukder MUddin MAkhter AKhalid M(2024)BrainNetInternational Journal of Intelligent Systems10.1155/2024/35836122024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/3583612
Keerthiveena BSheikh MKodamana HRathore A(2024)DeepDepth: Prediction of O(6)-methylguanine-DNA methyltransferase genotype in glioblastoma patients using multimodal representation learning based on deep feature fusionNeural Computing and Applications10.1007/s00521-024-09757-036:19(11507-11523)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09757-0
Verma AGupta NBhatele PKhanna P(2023)JMCD Dataset for Brain Tumor Detection and Analysis Using Explainable Deep LearningSN Computer Science10.1007/s42979-023-02308-94:6Online publication date: 3-Nov-2023
https://dl.acm.org/doi/10.1007/s42979-023-02308-9
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Index Terms

Deep CNN for Brain Tumor Classification
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Neural Processing Letters

Neural Processing Letters Volume 53, Issue 1

Feb 2021

845 pages

ISSN:1370-4621

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 February 2021

Accepted: 27 November 2020

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Cited By

Islam MTalukder MUddin MAkhter AKhalid M(2024)BrainNetInternational Journal of Intelligent Systems10.1155/2024/35836122024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/3583612
Keerthiveena BSheikh MKodamana HRathore A(2024)DeepDepth: Prediction of O(6)-methylguanine-DNA methyltransferase genotype in glioblastoma patients using multimodal representation learning based on deep feature fusionNeural Computing and Applications10.1007/s00521-024-09757-036:19(11507-11523)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09757-0
Verma AGupta NBhatele PKhanna P(2023)JMCD Dataset for Brain Tumor Detection and Analysis Using Explainable Deep LearningSN Computer Science10.1007/s42979-023-02308-94:6Online publication date: 3-Nov-2023
https://dl.acm.org/doi/10.1007/s42979-023-02308-9
Waheed SRahim MSuaib NSalim A(2023)RETRACTED ARTICLE: CNN deep learning-based image to vector depictionMultimedia Tools and Applications10.1007/s11042-023-14434-w82:13(20283-20302)Online publication date: 31-Jan-2023
https://dl.acm.org/doi/10.1007/s11042-023-14434-w
Chaddad AHassan LKatib Y(2023)A texture-based method for predicting molecular markers and survival outcome in lower grade gliomaApplied Intelligence10.1007/s10489-023-04844-653:21(24724-24738)Online publication date: 28-Jul-2023
https://dl.acm.org/doi/10.1007/s10489-023-04844-6
Alpar OKrejcar O(2023)Whole Tumor Area Estimation in Incremental Brain MRI Using Dilation and Erosion-Based Binary MorphingBioinformatics and Biomedical Engineering10.1007/978-3-031-34953-9_10(131-142)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-34953-9_10
Amin JAnjum MSharif MJabeen SKadry SMoreno Ger P(2022)A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational ClassifierComputational Intelligence and Neuroscience10.1155/2022/32363052022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/3236305
Tiwari PPant BElarabawy MAbd-Elnaby MMohd NDhiman GSharma S(2022)CNN Based Multiclass Brain Tumor Detection Using Medical ImagingComputational Intelligence and Neuroscience10.1155/2022/18300102022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1830010
Kumar KBoda R(2022)A computer-aided brain tumor diagnosis by adaptive fuzzy active contour fusion model and deep fuzzy classifierMultimedia Tools and Applications10.1007/s11042-022-12213-781:18(25405-25441)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11042-022-12213-7
Sasank VVenkateswarlu S(2022)Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI imagesMultimedia Tools and Applications10.1007/s11042-022-12106-981:6(8021-8057)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-022-12106-9
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