On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images
A brain tumor need to be identified in its early stage, otherwise it may cause severe condition that cannot be cured once it is progressed. A precise diagnosis of brain tumor can play an important role to start the proper treatment, which eventually reduces the survival rate of patient. Recently, de...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9785791/ |
| _version_ | 1828821203842760704 |
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| author | Saif Ahmad Pallab K. Choudhury |
| author_facet | Saif Ahmad Pallab K. Choudhury |
| author_sort | Saif Ahmad |
| collection | DOAJ |
| description | A brain tumor need to be identified in its early stage, otherwise it may cause severe condition that cannot be cured once it is progressed. A precise diagnosis of brain tumor can play an important role to start the proper treatment, which eventually reduces the survival rate of patient. Recently, deep learning based classification method is popularly used for brain tumor detection from 2D Magnetic Resonance (MR) images. In this article, several transfer learning based deep learning methods are analyzed using number of traditional classifiers to detect the brain tumor. The investigation results are based on a labeled dataset with the images of both normal- and abnormal brain. For transfer learning, seven methods are used such as VGG-16, VGG-19, ResNet50, InceptionResNetV2, InceptionV3, Xception, and DenseNet201. Each of them is followed by five traditional classifiers, which are Support Vector Machine, Random Forest, Decision Tree, AdaBoost, and Gradient Boosting. All the combinations of deep learning based feature extractor and classifier are investigated to evaluate the relevant performance in terms of accuracy, precision, recall, F1-score, Cohen’s kappa, AUC, Jaccard, and Specificity. Later on, learning curves for all of the combinations that achieved the highest accuracies were presented. The presented results show that the best model achieved an accuracy of 99.39% with a 10-fold cross validation. The results presented in this article are expected to be useful for the selection of suitable method in deep transfer learning based brain tumor detection. |
| first_indexed | 2024-12-12T12:49:51Z |
| format | Article |
| id | doaj.art-d3f2016ba5e64581a7a684040e77b638 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T12:49:51Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d3f2016ba5e64581a7a684040e77b6382022-12-22T00:24:01ZengIEEEIEEE Access2169-35362022-01-0110590995911410.1109/ACCESS.2022.31793769785791On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR ImagesSaif Ahmad0https://orcid.org/0000-0001-8552-6818Pallab K. Choudhury1https://orcid.org/0000-0002-3795-2844Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology (KUET), Khulna, BangladeshDepartment of Electronics and Communication Engineering, Khulna University of Engineering & Technology (KUET), Khulna, BangladeshA brain tumor need to be identified in its early stage, otherwise it may cause severe condition that cannot be cured once it is progressed. A precise diagnosis of brain tumor can play an important role to start the proper treatment, which eventually reduces the survival rate of patient. Recently, deep learning based classification method is popularly used for brain tumor detection from 2D Magnetic Resonance (MR) images. In this article, several transfer learning based deep learning methods are analyzed using number of traditional classifiers to detect the brain tumor. The investigation results are based on a labeled dataset with the images of both normal- and abnormal brain. For transfer learning, seven methods are used such as VGG-16, VGG-19, ResNet50, InceptionResNetV2, InceptionV3, Xception, and DenseNet201. Each of them is followed by five traditional classifiers, which are Support Vector Machine, Random Forest, Decision Tree, AdaBoost, and Gradient Boosting. All the combinations of deep learning based feature extractor and classifier are investigated to evaluate the relevant performance in terms of accuracy, precision, recall, F1-score, Cohen’s kappa, AUC, Jaccard, and Specificity. Later on, learning curves for all of the combinations that achieved the highest accuracies were presented. The presented results show that the best model achieved an accuracy of 99.39% with a 10-fold cross validation. The results presented in this article are expected to be useful for the selection of suitable method in deep transfer learning based brain tumor detection.https://ieeexplore.ieee.org/document/9785791/Brain tumormagnetic resonance imaging (MRI)transfer learningdeep learningVGG-19support vector machine (SVM) |
| spellingShingle | Saif Ahmad Pallab K. Choudhury On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images IEEE Access Brain tumor magnetic resonance imaging (MRI) transfer learning deep learning VGG-19 support vector machine (SVM) |
| title | On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images |
| title_full | On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images |
| title_fullStr | On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images |
| title_full_unstemmed | On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images |
| title_short | On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images |
| title_sort | on the performance of deep transfer learning networks for brain tumor detection using mr images |
| topic | Brain tumor magnetic resonance imaging (MRI) transfer learning deep learning VGG-19 support vector machine (SVM) |
| url | https://ieeexplore.ieee.org/document/9785791/ |
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