Next Article in Journal
Bioactive Peptides from Lupinus spp. Seed Proteins-State-of-the-Art and Perspectives
Previous Article in Journal
Structural Behaviour of Axially Loaded Concrete-Filled Steel Tube Columns during the Top-Down Construction Method
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 12
Issue 8
10.3390/app12083773
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network

by
Haitham Alsaif
1,
Ramzi Guesmi
2,*,
Badr M. Alshammari
1,
Tarek Hamrouni
3,
Tawfik Guesmi
1,
Ahmed Alzamil
1 and
Lamia Belguesmi
4
1
College of Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
2
Modeling Optimization and Augmented Engineering, Dep. Computer Science, ISLAI Béja, University of Jendouba, Béja 9000, Tunisia
3
LIPAH, Department of Computer Sciences, Faculty of Sciences of Tunis, Tunis El Manar University, Tunis 1068, Tunisia
4
Laboratory of Electronics and Information Technology, National Engineering School of Sfax, Sfax University, Sfax 3038, Tunisia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(8), 3773; https://doi.org/10.3390/app12083773
Submission received: 26 February 2022 / Revised: 1 April 2022 / Accepted: 3 April 2022 / Published: 8 April 2022
Browse Figures
Versions Notes

Abstract

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.
Keywords: data augmentation; brain tumor; deep learning; convolutional neural network; MRI data augmentation; brain tumor; deep learning; convolutional neural network; MRI

Share and Cite

MDPI and ACS Style

Alsaif, H.; Guesmi, R.; Alshammari, B.M.; Hamrouni, T.; Guesmi, T.; Alzamil, A.; Belguesmi, L. A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network. Appl. Sci. 2022, 12, 3773. https://doi.org/10.3390/app12083773

AMA Style

Alsaif H, Guesmi R, Alshammari BM, Hamrouni T, Guesmi T, Alzamil A, Belguesmi L. A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network. Applied Sciences. 2022; 12(8):3773. https://doi.org/10.3390/app12083773

Chicago/Turabian Style

Alsaif, Haitham, Ramzi Guesmi, Badr M. Alshammari, Tarek Hamrouni, Tawfik Guesmi, Ahmed Alzamil, and Lamia Belguesmi. 2022. "A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network" Applied Sciences 12, no. 8: 3773. https://doi.org/10.3390/app12083773

APA Style

Alsaif, H., Guesmi, R., Alshammari, B. M., Hamrouni, T., Guesmi, T., Alzamil, A., & Belguesmi, L. (2022). A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network. Applied Sciences, 12(8), 3773. https://doi.org/10.3390/app12083773

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop