A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network
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 u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/8/3773 |
| _version_ | 1797437050916438016 |
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| author | Haitham Alsaif Ramzi Guesmi Badr M. Alshammari Tarek Hamrouni Tawfik Guesmi Ahmed Alzamil Lamia Belguesmi |
| author_facet | Haitham Alsaif Ramzi Guesmi Badr M. Alshammari Tarek Hamrouni Tawfik Guesmi Ahmed Alzamil Lamia Belguesmi |
| author_sort | Haitham Alsaif |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:13:23Z |
| format | Article |
| id | doaj.art-d6308634aed544a780e6682a4ea94412 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:13:23Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d6308634aed544a780e6682a4ea944122023-12-01T00:38:24ZengMDPI AGApplied Sciences2076-34172022-04-01128377310.3390/app12083773A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural NetworkHaitham Alsaif0Ramzi Guesmi1Badr M. Alshammari2Tarek Hamrouni3Tawfik Guesmi4Ahmed Alzamil5Lamia Belguesmi6College of Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaModeling Optimization and Augmented Engineering, Dep. Computer Science, ISLAI Béja, University of Jendouba, Béja 9000, TunisiaCollege of Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaLIPAH, Department of Computer Sciences, Faculty of Sciences of Tunis, Tunis El Manar University, Tunis 1068, TunisiaCollege of Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaCollege of Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaLaboratory of Electronics and Information Technology, National Engineering School of Sfax, Sfax University, Sfax 3038, TunisiaBrain 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.https://www.mdpi.com/2076-3417/12/8/3773data augmentationbrain tumordeep learningconvolutional neural networkMRI |
| spellingShingle | Haitham Alsaif Ramzi Guesmi Badr M. Alshammari Tarek Hamrouni Tawfik Guesmi Ahmed Alzamil Lamia Belguesmi A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network Applied Sciences data augmentation brain tumor deep learning convolutional neural network MRI |
| title | A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network |
| title_full | A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network |
| title_fullStr | A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network |
| title_full_unstemmed | A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network |
| title_short | A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network |
| title_sort | novel data augmentation based brain tumor detection using convolutional neural network |
| topic | data augmentation brain tumor deep learning convolutional neural network MRI |
| url | https://www.mdpi.com/2076-3417/12/8/3773 |
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