An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network
Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three ph...
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| Format: | Article |
| Language: | English |
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University of Baghdad
2018-05-01
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| Series: | Ibn Al-Haitham Journal for Pure and Applied Sciences |
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| Online Access: | https://jih.uobaghdad.edu.iq/index.php/j/article/view/1834 |
| _version_ | 1811308044830638080 |
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| author | Ahmed Talib Abdulameer |
| author_facet | Ahmed Talib Abdulameer |
| author_sort | Ahmed Talib Abdulameer |
| collection | DOAJ |
| description |
Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers.
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| first_indexed | 2024-04-13T09:16:11Z |
| format | Article |
| id | doaj.art-fbc9301963c64e4aba5f14e5296aee63 |
| institution | Directory Open Access Journal |
| issn | 1609-4042 2521-3407 |
| language | English |
| last_indexed | 2024-04-13T09:16:11Z |
| publishDate | 2018-05-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Ibn Al-Haitham Journal for Pure and Applied Sciences |
| spelling | doaj.art-fbc9301963c64e4aba5f14e5296aee632022-12-22T02:52:44ZengUniversity of BaghdadIbn Al-Haitham Journal for Pure and Applied Sciences1609-40422521-34072018-05-0131110.30526/31.1.1834An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural NetworkAhmed Talib Abdulameer Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers. https://jih.uobaghdad.edu.iq/index.php/j/article/view/1834MRI Brain Images, Artificial Neural Network, Dragonfly Algorithm, Backpropagation Training Algorithm, Principal Component Analysis. |
| spellingShingle | Ahmed Talib Abdulameer An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network Ibn Al-Haitham Journal for Pure and Applied Sciences MRI Brain Images, Artificial Neural Network, Dragonfly Algorithm, Backpropagation Training Algorithm, Principal Component Analysis. |
| title | An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network |
| title_full | An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network |
| title_fullStr | An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network |
| title_full_unstemmed | An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network |
| title_short | An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network |
| title_sort | improvement of mri brain images classification using dragonfly algorithm as trainer of artificial neural network |
| topic | MRI Brain Images, Artificial Neural Network, Dragonfly Algorithm, Backpropagation Training Algorithm, Principal Component Analysis. |
| url | https://jih.uobaghdad.edu.iq/index.php/j/article/view/1834 |
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