Hybrid intelligent techniques for MRI brain images classification

El‐Dahshan, El‐Sayed A.; Hosny, Tamer; Salem, Abdel-Badeeh M.

doi:10.1016/j.dsp.2009.07.002

Cited by 480 publications

(240 citation statements)

References 10 publications

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“…In [14], Maitra and Chatterjee have shown that Slantlet transform can be combined with supervised classification (back-propagation neural network (BPNN)) technique to achieve 100% classification accuracy. Principal component analysis (PCA) is used to reduce the dimension of the feature vector obtained through DWT in [15] by El-Dahshan et al They have achieved 97% and 98% successrates through feed-forward BPNN and k-nearest neighbor (kNN) classifiers, respectively. Recently, Zhang et al have proposed several advanced techniques for brain MR image classification with high classification accuracies [12,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Das¹,

Chowdhury²,

Kundu³

2013

PIER

110

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Abstract-We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an efficient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 × 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classification accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is efficient in brain MR image classification.

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Section: Introductionmentioning

confidence: 99%

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Das¹,

Chowdhury²,

Kundu³

2013

PIER

110

View full text Add to dashboard Cite

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“…Similar experiment is performed by El Dashan et al [13] who categorized normal and abnormal regions of 80 ROIs using principal component analysis (PCA) for feature reduction and FP-ANN classifier. An accuracy of 97 and 98 % is obtained.…”

Section: Introductionmentioning

confidence: 86%

“…Some researchers have differentiated between two classes-normal (non-tumor) and abnormal (tumor) tissues [8,13]. Selveraj et al…”

Section: Introductionmentioning

confidence: 99%

Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification

et al. 2013

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Multiclass brain tumor classification is performed by using a diversified dataset of 428 post-contrast T1-weighted MR images from 55 patients. These images are of primary brain tumors namely astrocytoma (AS), glioblastoma multiforme (GBM), childhood tumor-medulloblastoma (MED), meningioma (MEN), secondary tumor-metastatic (MET), and normal regions (NR). Eight hundred fifty-six regions of interest (SROIs) are extracted by a content-based active contour model. Two hundred eighteen intensity and texture features are extracted from these SROIs. In this study, principal component analysis (PCA) is used for reduction of dimensionality of the feature space. These six classes are then classified by artificial neural network (ANN). Hence, this approach is named as PCA-ANN approach. Three sets of experiments have been performed. In the first experiment, classification accuracy by ANN approach is performed. In the second experiment, PCA-ANN approach with random sub-sampling has been used in which the SROIs from the same patient may get repeated during testing. It is observed that the classification accuracy has increased from 77 to 91 %.PCA-ANN has delivered high accuracy for each class: A S -9 0 . 7 4 % , G B M -8 8 . 4 6 % , M E D -8 5 % , MEN-90.70 %, MET-96.67 %, and NR-93.78 %. In the third experiment, to remove bias and to test the robustness of the proposed system, data is partitioned in a manner such that the SROIs from the same patient are not common for training and testing sets. In this case also, the proposed system has performed well by delivering an overall accuracy of 85.23 %. The individual class accuracy for each class is: AS-86.15 %, GBM-65.1 %, MED-63.36 %, MEN-91.5 %, MET-65.21 %, and NR-93.3 %. A computer-aided diagnostic system comprising of developed methods for segmentation, feature extraction, and classification of brain tumors can be beneficial to radiologists for precise localization, diagnosis, and interpretation of brain tumors on MR images.

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“…We compared the proposed HBP with BP [37], MBP [38], GA [39], SA [40], 2.4.1 [41], BBO [42], PSO [43], and BPSO [50] III. We compared the proposed HBP-FNN with fourteen state-of-the-art classification methods as DWT + PCA + FP-ANN [7], DWT + PCA + KNN [7], DWT + PCA + SCABC-FNN [8], DWT + PCA + SVM + HPOL [11], DWT + PCA + SVM + IPOL [11], DWT + PCA + SVM + GRB [11], WE + SWP + PNN [12], RT + PCA + LS-SVM [14], PCNN + DWT + PCA + BPNN [17], DWPT + SE + GEPSVM [18], DWPT + TE + GEPSVM [18], WE + NBC [19], WEnergy + SVM [22], and SWT + PCA + HPA-FNN [26]. IV.…”

Section: Experiments Designmentioning

confidence: 99%

“…Then, they used the common back-propagation neural network (BPNN). El-Dahshan et al [7] extracted the approximation and detail coefficients of 3-level DWT, reduced the coefficients by principal component analysis (PCA), and used feed-forward back propagation artificial neural network (FP-ANN) and K-nearest neighbors (KNN) classifiers. Zhang et al [8] proposed using DWT for feature extraction, PCA for feature reduction, and FNN with scaled chaotic artificial bee colony (SCABC) as classifier.…”

Section: Existing Pathological Brain Detection Systemsmentioning

confidence: 99%

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

Zhang¹,

Wang²,

Dong³

et al. 2015

PIER

110

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Abstract-(Background)We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.

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Hybrid intelligent techniques for MRI brain images classification

Cited by 480 publications

References 10 publications

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

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