Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

El‐Dahshan, El‐Sayed A.; Mohsen, Heba; Revett, Kenneth; Salem, Abdel-Badeeh M.

doi:10.1016/j.eswa.2014.01.021

Cited by 560 publications

(266 citation statements)

References 54 publications

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“…E.S. A. E. Dahshan et al [19] made a State-of-the-Art review works on CAD systems developed during the year 2005-2015 and also proposed a hybrid intelligent machine learning technique in which they used principle component analysis (PCA) for reducing the Wavelet features and Feedforward multilayer neural network (FFNN) for classification for automatic detection of brain tumor. M.S.…”

Section: Related Workmentioning

confidence: 99%

Grammatical Swarm based Segmentation Methodology for Lesion Segmentation in Brain MRI

Si¹,

De²,

Bhattacharjee³

2015

IJCA

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This paper presents Grammatical Swarm based segmentation methodology for lesion detection in brain's magnetic resonance image. In the proposed methodology, images are denoised using median filter at the outset. Secondly, images are segmented using Grammatical Swarm based hard-clustering technique. Finally, lesions are extracted from the segmented images. The proposed methodology is applied on six Axial-T2 magnetic resonance images and compared with Particle Swarm Optimizer, K-Means and FCM based segmentation methods using quantitative performance measurement index. The experimental results show that the proposed methodology statistically outperforms other methods.

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Section: Related Workmentioning

confidence: 99%

Grammatical Swarm based Segmentation Methodology for Lesion Segmentation in Brain MRI

Si¹,

De²,

Bhattacharjee³

2015

IJCA

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“…Padma and Sukanesh [16] used combined wavelet statistical texture features, to segment and classify Alzheimer's disease (AD) benign and malignant tumor slices. El-Dahshan et al [17] used the feedback pulse-coupled neural network for image segmentation, the DWT for features extraction, the PCA for reducing the dimensionality of the wavelet coefficients, and the FBPNN to classify inputs into normal or abnormal. The classification accuracy on both training and test images is 99%, which was significantly good.…”

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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“…As a result, these methods were used either as a preprocessing step in the segmentation of brain tumor or refinement step [4]. Recently, these techniques have been combined with artificial neural networks (ANNs) [5], genetic algorithm (GA) [6], fuzzy logic [7], and Markov model. Supervised techniques such as ANN and support vector machine (SVM) are used in classification methods.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images

Eltayeb¹,

Salem²,

Al-Atabany³

2018

IJCA

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In this paper, a study for evaluating the efficacy of different feature sets that used brain tumor classification is presented. Different features sets are extracted as shape, 1 st order texture features (FOS), 2 nd order (GLCM, GLRLM), boundary features, and wavelet-based features. The brain tumors are extracted using the k-means clustering algorithm. Then different classifiers such as Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) were used in the classification process.A set of 65 real and simulated (Flair modality) MRI images from multimodal brain tumor image segmentation benchmark (BRATS) organized by MICCAI 2012 challenge is used for performance evaluation. The overall segmentation results for the 65 volumes are 90.15±0.12. For the Feature sets efficacy step, the highest accuracy of 94.74% is achieved by the SVM when using the wavelet-based features. The lowest accuracy achieved by the three classifiers obtained when using the second order texture features..

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Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

Cited by 560 publications

References 54 publications

Grammatical Swarm based Segmentation Methodology for Lesion Segmentation in Brain MRI

Grammatical Swarm based Segmentation Methodology for Lesion Segmentation in Brain MRI

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

Evaluating the Efficiency of different Feature Sets on Brain Tumor Classification in MR Images

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