Article

An Mr Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

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Abstract

Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this paper, we presented a novel method to classify a given MR brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of K-fold stratified cross validation was used to enhance generalization of KSVM. We chose seven common brain diseases (glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick's disease, sarcoma, and Huntington's disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Harvard Medical School website. We performed our proposed methods with four different kernels, and found that the GRB kernel achieves the highest classification accuracy as 99.38%. The LIN, HPOL, and IPOL kernel achieves 95%, 96.88%, and 98.12%, respectively. We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernel still achieved the best accurate classification results. The averaged processing time for a 256 × 256 size image on a laptop of P4 IBM with 3 GHz processor and 2 GB RAM is 0.0448 s. From the experimental data, our method was effective and rapid. It could be applied to the field of MR brain image classification and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees.

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... Because it allows for image analysis at multiple levels of resolution, the wavelet transform is an excellent technique for extracting characteristics from Magnetic Resonance (MR) brain images. As a result of its multi-resolution analytical capacity [1]. For this purpose, researchers have submitted several studies, all of which have yielded positive results. ...
... Multifilter construction approaches are already being developed to make use of them [12][13][14]. The objective of this multiplicity is to achieve the characteristics described below [1]. ...
... DWT provides local signal time-frequency information with cascaded high-pass and low-pass filter banks to hierarchically retrieved features, enabling the extraction of the most relevant features of varied pathways and dimensions [19]. The concept of Multiwavelet was born out of the generalization of scalar wavelets [1,2]. Multiple wavelets and scalar functions are used instead of a single scalar one. ...
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A brain tumor is one of the most common and deadly diseases in the current era, which requires early and reliable detection techniques. The diagnosis of brain tumors depends on the analysis of Magnetic Resonance Imaging (MRI). It is a technology that provides the highest and most realistic images of the anatomical structures of the human body, particularly the brain. It includes information that can be used in place of a clinical diagnosis. It is a reliable method and does Citation: Al-Jawher WAM, Al-taee SHA (2022) Precise Classification of Brain Magnetic Resonance Imaging (MRIs) using Gray Wolf Optimization (GWO) J Brain Neursci 6: 021.
... Because it allows for image analysis at multiple levels of resolution, the wavelet transform is an excellent technique for extracting characteristics from Magnetic Resonance (MR) brain images. As a result of its multi-resolution analytical capacity [1]. For this purpose, researchers have submitted several studies, all of which have yielded positive results. ...
... Multifilter construction approaches are already being developed to make use of them [12][13][14]. The objective of this multiplicity is to achieve the characteristics described below [1]. ...
... DWT provides local signal time-frequency information with cascaded high-pass and low-pass filter banks to hierarchically retrieved features, enabling the extraction of the most relevant features of varied pathways and dimensions [19]. The concept of Multiwavelet was born out of the generalization of scalar wavelets [1,2]. Multiple wavelets and scalar functions are used instead of a single scalar one. ...
Article
A brain tumor is one of the most common and deadly diseases in the current era, which requires early and reliable detection techniques. The diagnosis of brain tumors depends on the analysis of Magnetic Resonance Imaging (MRI). It is a technology that provides the highest and most realistic images of the anatomical structures of the human body, particularly the brain. It includes information that can be used in place of a clinical diagnosis. It is a reliable method and does Citation: Al-Jawher WAM, Al-taee SHA (2022) Precise Classification of Brain Magnetic Resonance Imaging (MRIs) using Gray Wolf Optimization (GWO) J Brain Neursci 6: 021.
... In Step 1, we first used Eq. (1) to delineate the KEDF landscape of an input brain tumor image I ∈ R H×W acquired from an open-source dataset 33 . Then we constructed the kernel k r ′ ; r based on its spatial characteristics in 2-dimensional Euclidean. ...
... Moreover, magnet www.nature.com/scientificreports/ Step 1, the mapping of the FLAIR image 33 constructs the KEDF landscape, followed by forming the reciprocal distance kernel (RDK). In Step 2, element-wise products of 2D-FFTs of the image and kernel are calculated and then inversed for PEDF estimation. ...
... The CPU time for this small dataset ranges between 0.06 to 0.09 s. We acquired these images from an open-source dataset 33 www.nature.com/scientificreports/ like several low-energy impurities filled below the Fermi surface; they obstruct the electron transmission along the surface and thus cover the exertion of intrinsic material properties. ...
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... The factual technique for finding the surfaces to consider the spatial association of the pixels is Gray Level Co-Occurrence Matrix abbreviated as GLCM. [4] The target of GLCM is to recognize the outside of a picture by assessing the pixel in pair with explicit qualities and determined spatial relationship. GLCM helps in estimating the factual extricated parameters. ...
... Every segment of the pixels which comes under the mask is grouped together to represent a single pixel value and this grouping is based on average values of all pixels inside mask. (4) Where N is the number of pixels inside the window and Xi represents each pixel. ...
... Support Vector Machine Classifier is used to classify classes based on a hyper-plane which is similar to a line cutting a plane into two parts and hence also known as a discriminative classifier. [4] Each class is on either side of the hyper plane. Thus SVM is used when the input data is to be classified among these classes only. ...
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... The model uses labeled data to learn the structure of the problem and then uses the unlabeled data to fine-tune its predictions [1]. Neural networks (NN) [2], support vector machine [3], K-nearest neighbour, and linear regression are widely used supervised learning algorithms. An artificial neural network (ANN) is a computational model which is inspired by the human brain. ...
... Twodimensional (2D) DWT is a versatile signal-processing tool formulated by Daubechies and Mallet in the late 1980s. DWT splits a signal into low-frequency and high-frequency parts, called decomposition [3]. DWT decomposition of an image into low resolution, i.e., approximation coefficients (LL subband) and high resolution, i.e., detail coefficients (the horizontal HL, the vertical LH, and the diagonal HH subbands). ...
... The results obtained in this paper using the KMC+DWT+SCGBPNN algorithm and KMC+DWT+BRBPNN algorithm are compared with other existing literature, i.e., DWT+HC+BPNN [9], DWT+HC+SVM [9], DWT+SOM [16], DWT+linear kernel SVM [16], DWT+PCA+Linear kernel SVM [3], DWT+PCA+KSVM(HPOL) [3], and DWT+PCA+ANN [2]. Table 6 shows the compilation of various image classification algorithms, the number of images in the dataset, and classification accuracy. ...
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... Other advantages of quadratic kernels include having fine mathematical tractability and direct geometric representation. 19,[22][23][24][25] K -Nearest neighbors. K-NN is a popular classifier used commonly because of its simplicity, straightforwardness, and high efficiency despite noisy data. ...
... The kerosene is loaded through three loading pumps (PUMP_4,5,6). Each loading pump is fed by kerosene through a suction valve (MOV_20, 22,24) and discharged through a discharge valve (MOV_21, 23,25). The Auto/ Manual Push buttons are used to select the control mode of the loading pump. ...
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... Other advantages of quadratic kernels include having fine mathematical tractability and direct geometric representation. 19,[22][23][24][25] K -Nearest neighbors. K-NN is a popular classifier used commonly because of its simplicity, straightforwardness, and high efficiency despite noisy data. ...
... The kerosene is loaded through three loading pumps (PUMP_4,5,6). Each loading pump is fed by kerosene through a suction valve (MOV_20, 22,24) and discharged through a discharge valve (MOV_21, 23,25). The Auto/ Manual Push buttons are used to select the control mode of the loading pump. ...
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... Surgery is used to remove the aberrant brain cells, and subsequent treatment with medication is intensive. An abnormal development of tissue that may be seen in the brain is called a tumor [10][11][12][13][14]. Brain tumors are very rare, and unlike tumors in other regions of the body, they can only spread from one brain cell to another inside the brain. ...
Article
The article describes the use of image processing in the search for the brain tumor. Tumors are a worldwide health crisis that may manifest in any part of the body. If a brain tumor is not diagnosed and treated early on, it may significantly reduce a patient's lifespan. Malignant and benign tumors of varied stages were discovered. This illness is now affecting a sizable population. Each year, more and more people are screened every day in an effort to discover diseases early. Manual screening is not only a time-consuming process, but it also raises the possibility of making mistakes. Some people could become even more distracted. Therefore, it is more ideal to use an AI-based system for the screening process than a human one. Specialists employ MRI (Magnetic Resonance Imaging) scan images to identify brain cancers; however, these images include noise that must be minimized in the first phases of processing if accurate findings are to be achieved. In this research, we provide a refined Discrete Wavelet Transform (DWT) filtering technique along the used of artificial Intelligence for this purpose. Image filtering and image segmentation are the system's two main components. Brain tumor MRI images from the Kaggle dataset will be used as test data for the filter.
... Despite the advancement of wavelet analysis, several wavelet types have become more and more well-known. The most important wavelet is the Harr wavelet, which is the simplest one and often the preferred wavelet in a lot of applications 45 . Equation (3) and Eq. ...
... Despite the advancement of wavelet analysis, several wavelet types have become more and more well-known. The most important wavelet is the Harr wavelet, which is the simplest one and often the preferred wavelet in a lot of applications 45 . Equation (3) and Eq. ...
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... Following the evaluation process, an impressive accuracy of 99% was achieved. Similarly, the authors of [80] [81] [82] [8] [83] used PCA for feature reduction. It is a statistical technique employed in data analysis to simplify complex datasets. ...
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... This has been made possible by the fact that ACO adopts filters that enable it select only those features that are most relevant in improving the accuracy and robustness of these models for the purpose of early diagnosis. Furthermore, ACO can easily scale medical data with numerous dimensions such as image medical data, genetic information and patients' histories, which are applied in predictive models of lung cancer [36][37]. This shows that ACO can be effective in these applications to be placed as highly efficient optimization tool in the sector of medical diagnostics. ...
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... It is possible to make images with great contrast by adjusting the radio frequency and gradient pulse, for example. There are two types of brain tumors: benign and malignant [3]. Non-cancerous tumors are benign, and cancerous tumors, called malignant tumors, are more likely to develop as a result of cancer in any region of the body, not just the brain. ...
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... But this method can differentiate only glioblastoma, sarcoma and metastatic bronchogenic carcinoma tumors. Zhang and Wu [5] proposed a classification method by using kernel support vector machine (KSVM). Wavelet transforms as DWT was used to extract features from images and reduced the dimensions of those features by PCA method. ...
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... A brain tumor is described as an abnormal growth of tissue [10][11][12][13][14]. Brain tumors, unlike malignancies in other regions of the body, can only spread within the brain. ...
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... It interprets this information to create our perception of the world around us. (ii) Motor Control: The brain controls voluntary and involuntary movements of the body [7]. It sends signals to muscles and other organs through the nervous system to enable movement, from simple actions like picking up an object to complex tasks like playing a musical instrument [8]. (iii) Homeostasis: The brain helps maintain the body's internal balance (homeostasis) by regulating functions like body temperature, blood pressure, and hormone production [9][10][11]. ...
Preprint
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... For the past 40 decades, it is used as a significant tool in scientific research and medicine diagnose. MRI is the most suitable choice for investigating brain tumor as it is more sensitive than CT scan in identifying small tumors and it gives better visualization [4]. ...
Research
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4) 2: 730-738] © 2018 IJSRST | Volume 4 | Issue 2 | Print ABSTRACT A Brain tumor is very serious disease causing deaths of many individuals. The detection and classification system must be available so that it can be diagnosed at early stages. Detection of the brain tumor and its type in its early stage is essential for right treatment. So classification of brain tumor is very important. Tumor classification has been one of the most challenging tasks in clinical diagnosis. Different image processing techniques such as image segmentation, image enhancement and feature extraction are used for detection of the brain tumor in the MRI images of the cancer affected patients. Medical Image Processing is the fast growing and challenging field now days. Image processing and neural network techniques are used to improve the performance of detecting and classifying brain tumor in MRI images. The objective of this review paper is to presents a comprehensive overview for MRI brain tumor segmentation methods. In this paper, various segmentation techniques have been discussed. Comparative analysis of existing techniques has been done in brief.
... [17] presented a Support Vector Machine (SVM)-Active learning technique that allows the operator to tag chosen images that are closer to the SVM border to train the SVM. According to [53], when all of the feedback rounds have been completed, the SVM is trained to extract the top n imageries that are distant from the support vector machine border. [36] put forward a content-centric RS for retrieving images. ...
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... It is used here to locate the necessary signal information to classify our mammogram images. Thus, we used the 2D discrete wavelet transform, which led to four sub-bands Low-Low (LL), High-Low (HL), Low-High(LH), High-High(HH), with a three-level decomposition of our image [20]. The wavelet approximations at the first and second levels are represented by LL1, LL2 respectively. ...
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Radiologists use mammogram images for the diagnosis of breast cancer. However, interpreting these images remains challenging depending on the type of breast, especially on dense breasts. Dense breasts may contain abnormal structures similar to normal breast tissue and could lead to a high rate of false positives and false positives negatives. We present an efficient computer-aided diagnostic system for detecting and classifying breast masses. After removing noise and artifacts from the images using 2D median filtering, mathematical morphology and pectoral muscle removal by Hough's algorithm, the resulting image is used for breast mass segmentation using the watershed algorithm. After the segmentation, the system extracts several data by the wavelet transform and the co-occurrence matrix (GLCM) to finally lead to a classification as malign or benign mass via the Support Vector Machine (SVM) classifier. This method was applied to 48 Medio-Lateral Oblique (MLO) images from the image base (mini-MIAS). The algorithm showed a 87.5% classification rate, 92.59% sensitivity, and a specificity of 93.94%.
... In our study, we have applied a linear and quadratic kernel SVM to classify the datasets. QSVM is a nonlinear kernel with excellent mathematical adaptability and direct geometric explanation [28] that may outperform LSVM. LSVM and QSVM kernel equations are shown in Eq. (1) and (2) [29]. ...
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Breast cancer is one of the main causes of mortality for women around the world. Such mortality rate could be reduced if it is possible to diagnose breast cancer at the primary stage. It is hard to determine the causes of this disease that may lead to the development of breast cancer. But it is still important in predicting the probability of cancer. We can assess the likelihood of occurrence of breast cancer using machine learning algorithms and routine diagnosis data. Although a variety of patient information attributes are stored in cancer datasets not all of the attributes are important in predicting cancer. In such situations, feature selection approaches can be applied to keep the pertinent feature set. In this research, a comprehensive analysis of Machine Learning (ML) classification algorithms with and without feature selection on Wisconsin Breast Cancer Original (WBCO), Wisconsin Diagnosis Breast Cancer (WDBC), and Wisconsin Prognosis Breast Cancer (WPBC) datasets is performed for breast cancer prediction. We employed wrapper-based feature selection and three different classifiers Logistic Regression (LR), Linear Support Vector Machine (LSVM), and Quadratic Support Vector Machine (QSVM) for breast cancer prediction. Based on experimental results, it is shown that the LR classifier with feature selection performs significantly better with an accuracy of 97.1% and 83.5% on WBCO and WPBC datasets respectively. On WDBC datasets, the result reveals that the QSVM classifier without feature selection achieved an accuracy of 97.9% and these results outperform the existing methods.
... They achieved an accuracy of 96.51%. Zhang et al. [10] proposed an automatic method for classification of MRI brain images based kernel support vector machine (KSVM) and wavelet transform (WT) features with Principal Component Analysis (PCA) to reduce the size of features. Usman and Rajpoot [11] investigated wavelet texture features with random forest classifier to predict tumor labels as multiclass classification. ...
... 3 Brain tumor classification is a significant progressing investigation area. [5][6][7] Extensively two types of classification methods are such as supervised and unsupervised 8 strategies. Two major unsupervised methods are self-organization map (SOM) and fuzzy C-means (FCM) which cannot categorize difficult cancers as high-grade cancers. ...
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... Traditional methods generally extract a series of features such as statistical features and texture features of images, and then, use artificial neural networks, random forests, and support vector machines for segmentation [4]. Traditional methods can be roughly divided into four categories, namely threshold-based segmentation methods [5,6], edge-based segmentation methods [7][8][9], and cluster-based segmentation methods [10][11][12] and region-based segmentation methods [13,14]. However, the pros and cons of extracting features in traditional methods will greatly affect the final results of the experiment. ...
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... But this method can differentiate only glioblastoma, sarcoma and metastatic bronchogenic carcinoma tumors. Zhang and Wu [5] proposed a classification method by using kernel support vector machine (KSVM). Wavelet transforms as DWT was used to extract features from images and reduced the dimensions of those features by PCA method. ...
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... With the purpose of providing a full-resolution semantic prediction, a U-Net augments the normal CNN design by adding a comparable expanding path. The goal is to create segmentation images that highlight specific features and objects in the image [23]. ...
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... Discreate Wavelet Transform is employed to extract multiple features from the filtered image. Fourier transformation has a serious disadvantage as it doesn't contain any information about time domain [14]. LL, LH, HH, and HL are the four sub-bands for each scale [15]. ...
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... Therefore, many studies about systems to classify brain tumor types have been conducted. Zhang et al [1] extracted features with wavelet transform (WT) and reduced dimensions using principal component analysis (PCA), then classify the tumors with a support vector machine (SVM). Othman el al [2] proposed a probabilistic neural network with PCA to classify meningioma and glioma. ...
... Also, DWT was used with a naive Bayes classifier (NBC) to improve brain image categorization [27]. Most of the models employ DWT and PCA for feature extraction besides feature selection and are evaluated with different classifiers such as KNN, feed-forward backpropagation artificial neural network (FPANN) [28], forward neural network (FNN) with adaptive chaotic particle swarm optimization (ACPSO) [29] and scaled chaotic artificial bee colony (SCABC) [30], backpropagation neural network (BPNN) with scaled conjugate gradient (SCG) [31], kernel support vector machine (KSVM) with gaussian radial basis (GRB) kernel [32] and least squares support vector machine (LSSVM) [33]. A LSSVM was used with PCA in classifying the brain MR images that utilize Ripplet transform type for capturing the features [34]. ...
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... P. Kumar et al. [16] used four stage method for segmentation and classification in which principal component analysis (PCA) was utilized to reduce dimensionality and afterward SVM classifier was applied for segmentation and managed to achieve accuracy level of 94%. Similar method was also applied by Y. Zhang et al. [17] as he used kernel SVM and for enhancement K-fold stratified cross validation was applied. GUOLI SONG et al. [18] also did similar work but in addition applied GLCM for hybrid feature extraction and was able to attain 98.36% accuracy. ...
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Abstract Learning is key to developing systems tailored to a broad range of data analysis and information extraction tasks. We outline the mathematical foundations of learning theory and describe a key algorithm of it.
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During Magnetic Resonance Imaging (MRI), the presence of an implant such as a Deep Brain Stimulation (DBS) lead in a patient's body can pose a significant risk. This is due to the fact that the MR radiofrequency (RF) field can achieve a very high strength around the DBS electrodes. Thus the specific absorption rate (SAR), which is proportional to the square of the magnitude of the RF electric field, can have a very high concentration in the near-field region of the electrodes. The resulting tissue heating can reach dangerous levels. The degree of heating depends on the level of SAR concentration. The effects can be severe, leading to tissue ablation and brain damage, and significant safety concerns arise whenever a patient with an implanted DBS lead is exposed to MR scanning. In this paper, SAR, electric field, and temperature rise distributions have been found around actual DBS electrodes. The magnitude and spatial distribution of the induced temperature rises are found to be a function of the length and structure of the lead device, tissue properties and the MR stimulation paramete
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During the past decades there has been a tremendous increase throughout the scientific community for developing methods of understanding human brain functionality, as diagnosis and treatment of diseases and malfunctions, could be effectively developed through understanding of how the brain works. In parallel, research effort is driven on minimizing drawbacks of existing imaging techniques including potential risks from radiation and invasive attributes of the imaging methodologies. Towards that direction a new near field radiometry imaging system has been theoretically studied, developed and experimentally tested and all of the aforementioned research phases are herein presented. The system operation principle is based on the fact that human tissues emit chaotic thermal type radiation at temperatures above the absolute zero. Using a phase shifted antenna array system, spatial resolution, detection depth and sensitivity are increased. Combining previous research results, as well as new findings, the capabilities of the constructed system, as well as the possibility of using it as a complementary method for brain imaging are discussed in this paper.
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Automated and accurate classification of magnetic resonance (MR) brain images is an integral component of the analysis and interpretation of neuroimaging. Many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a forward neural network (FNN) based method to classify a given MR brain image as normal or abnormal. This method first employs a wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to an FNN, and these parameters are optimized via adaptive chaotic particle swarm optimization (ACPSO). K-fold stratified cross validation was used to enhance generalization. We applied the proposed method on 160 images (20 normal, 140 abnormal), and found that the classification accuracy is as high as 98.75% while the computation time per image is only 0.0452s.
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Automated and accurate classification of magnetic resonance (MR) brain images is a hot topic in the field of neuroimaging. Recently many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a hybrid method based on forward neural network (FNN) to classify an MR brain image as normal or abnormal. The method first employed a discrete wavelet transform to extract features from images, and then applied the technique of principle component analysis (PCA) to reduce the size of the features. The reduced features were sent to an FNN, of which the parameters were optimized via an improved artificial bee colony (ABC) algorithm based on both fitness scaling and chaotic theory. We referred to the improved algorithm as scaled chaotic artificial bee colony (SCABC). Moreover, the K-fold stratified cross validation was employed to avoid overfitting. In the experiment, we applied the proposed method on the data set of T2-weighted MRI images consisting of 66 brain images (18 normal and 48 abnormal). The proposed SCABC was compared with traditional training methods such as BP, momentum BP, genetic algorithm, elite genetic algorithm with migration, simulated annealing, and ABC. Each algorithm was run 20 times to reduce randomness. The results show that our SCABC can obtain the least mean MSE and 100% classification accuracy.
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In this paper we explain how to characterize the best approximation to any xx in a Hilbert space XX from the set C∩{x∈X:gi(x)≤0,i=1,2,…,m}C∩{x∈X:gi(x)≤0,i=1,2,…,m} in the face of data uncertainty in the convex constraints, gi(x)≤0,i=1,2,…,mgi(x)≤0,i=1,2,…,m, where CC is a closed convex subset of XX. Following the robust optimization approach, we establish Lagrange multiplier characterizations of the robust constrained best approximation that is immunized against data uncertainty. This is done by characterizing the best approximation to any xx from the robust counterpart of the constraints where the constraints are satisfied for all possible uncertainties within the prescribed uncertainty sets. Unlike the traditional Lagrange multiplier characterizations without data uncertainty, for constrained best approximation problems in the face uncertainty, we show that the strong conical hull intersection property (strong CHIP) alone is not sufficient to guarantee the Lagrange multiplier characterizations. We present conditions which guarantee that the strong CHIP is necessary and sufficient for the multiplier characterization. We also establish that the strong CHIP is automatically satisfied for the cases of polyhedral constraints with polytope uncertainty, and linear constraints with interval uncertainty. As an application, we show how robust solutions of shape preserving interpolation problems under ellipsoidal and box uncertainty cases can be obtained in terms of Lagrange multipliers under strict robust feasibility conditions.
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Feed forward neural Network (FNN) has been widely applied to many fields because of its ability to closely approximate unknown function to any degree of desired accuracy. Back Propagation (BP) is the most general learning algorithms, but is subject to local optimal convergence and poor performance even on simple problems when forecasting out of samples. Thus, we proposed an improved Bacterial Chemotaxis Optimization (BCO) approach as a possible alternative to the problematic BP algorithm, along with a novel adaptive search strategy to improve the efficiency of the traditional BCO. Taking the classical XOR problem and sinc function approximation as examples, comparisons were implemented. The results demonstrate that our algorithm is obviously superior in convergence rate and precision compared with other training algorithms, such as Genetic Algorithm (GA) and Taboo Search (TS).
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Automated animal behaviour monitoring systems have become increasingly appealing for research and animal production management purposes. However, many existing systems are suited to measure only one or two behaviour patterns or activity states at a time. We aimed to develop and pilot a method for automatically measuring and recognising several behavioural patterns of dairy cows using a three-dimensional accelerometer and a multi-class support vector machine (SVM). SVM classification models were constructed based on nine features. The models were trained using observations made of the behaviour of 30 cows fitted with a neck collar bearing an accelerometer that recorded horizontal, vertical and lateral acceleration. Measured behaviour patterns included standing, lying, ruminating, feeding, normal and lame walking, lying down, and standing up. Accuracy, sensitivity, precision, and kappa measures were used to evaluate the model performance. The SVM classification models achieved a reasonable recognition of standing (80% sensitivity, 65% precision), lying (80%, 83%), ruminating (75%, 86%), feeding (75%, 81%), walking normally (79%, 79%), and lame walking (65%, 66%). The results were poor for lying down (0%, 0%) and standing up (71%, 29%). The overall performance of the multi-class model was 78% precision with a kappa value of 0.69. Each of the behaviour categories had one or two other behaviour patterns that became confused with them the most. The problematic behaviours were expectedly those that resemble each other in terms of movement. Possible solutions for the problems in classification are presented. In conclusion, accelerometers can be used to easily recognise various behaviour patterns in dairy cows. Support vector machines proved useful in classification of measured behaviour patterns. However, further work is needed to refine the features used in the classification models in order to gain the best possible classification performance. Also the quality of acceleration data needs to be considered to improve the results.
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This paper proposes a hybrid classifier for polarimetric SAR images. The feature sets consist of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features are reduced by principle component analysis (PCA). A 3-layer neural network (NN) is constructed, trained by resilient back-propagation (RPROP) method to fasten the training and early stop (ES) method to prevent the overfitting. The results of San Francisco and Flevoland sites compared to Wishart Maximum Likelihood and wavelet-based method demonstrate the validness of our method in terms of confusion matrix and overall accuracy. In addition, NNs with and without PCA are compared. Results show the NN with PCA is more accurate and faster.
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Least Square Support Vector Machines (LS-SVM), a new machine-learning tool has been employed for developing data driven models of non-linear processes. The method is firmly rooted in the statistical learning theory and transforms the input data to a higher dimensional feature space where the use of appropriate kernel functions avoid computational difficulty. Further, a pattern search algorithm, which explores multiple directions and utilizes coordinate search with fixed step size, is employed for selecting optimal LS-SVM model that produces a minimum possible prediction error. To show the efficacy and efficiency of the fully automated pattern search assisted LS-SVM methodology, we have tested it on several benchmark examples. The study suggests that proposed paradigm can be a useful and viable tool in building data driven models of non-linear processes.
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Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
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An approach for the microwave nonlinear device modeling technique based on a combination of the conventional equivalent circuit model and support vector machine (SVM) regression is presented in this paper. The intrinsic nonlinear circuit elements are represented by Taylor series expansions, coefficients of which are predicted by its support vector regression (SVR) model. Example of a SiC MESFET nonlinear model is demonstrated, and good results is achieved.
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This manuscript aims at illustrating significant refinements concerning the use of wavelets, when these latter are used in the guise of continuous wavelet transforms (CWT) for identifying damage on transversally vibrating structural components (e.g. beams, plates and shells). The refinements regard the presentation of wavelet-algorithms which are aimed at significantly reducing those border distortions normally arising during a wavelet-damage detection procedure. The main advantage of the algorithms is that they are self-contained, namely: (i) the wavelet transforms do not undergo any own variation and their application follows the convolution laws established in the past; (ii) it is not necessary to design a specific boundary wavelet; (iii) no significant analytical treatment neither of the wavelets nor of the signal is required and, finally, (iv) the algorithms can be adapted to different boundary conditions and different physical situations. Besides all the specified advantages, the wavelet-damage detection procedure is still carried out by excluding historical data. The effectiveness of the algorithms is shown through numerical and experimental examples. These latter are illustrated along with reduced outliers of experimental estimation through a related consistent statistical procedure.
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Deep brain stimulation (DBS) is a well-established treatment for Parkinson's disease, essential tremor and dystonia. It has also been successfully applied to treat various other neurological and psychiatric conditions including depression and obsessive-compulsive disorder. Numerous computational models, mostly based on the Finite Element Method (FEM) approach have been suggested to investigate the biophysical mechanisms of electromagnetic wave-tissue interaction during DBS. These models, although emphasizing the importance of various electrical and geometrical parameters, mostly have used simplified geometries over a tightly restricted tissue volume in the case of monopolar stimulation. In the present work we show that topological arrangements and geometrical properties of the model have a significant effect on the distribution of voltages in the concerned tissues. The results support reconsidering the current approach for modeling monopolar DBS which uses a restricted cubic area extended a few centimeters around the active electrode to predict the volume of activated tissue. We propose a new technique called multi-resolution FEM modeling, which may improve the accuracy of the prediction of volume of activated tissue and yet be computationally tractable on personal computers.
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A new method of reducing the computational load in decision functions provided by a support vector classification machine is studied. The method exploits the geometrical relations when the kernels used are based on distances to obtain bounds of the remaining decision function and avoids to continue calculating kernel operations when there is no chance to change the decision. The method proposed achieves savings in operations of 25–90% whilst keeping the same accuracy. Although the method is explained for support vector machines, it can be applied to any kernel binary classifier that provides a similar evaluation function.
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A main theme of this report is the relationship of approximation to learning and the primary role of sampling (inductive inference). We try to emphasize relations of the theory of learning to the main stream of mathematics.
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Hitherto communication theory was based on two alternative methods of signal analysis. One is the description of the signal as a function of time; the other is Fourier analysis. Both are idealizations, as the first method operates with sharply defined instants of time, the second with infinite wave-trains of rigorously defined frequencies. But our everyday experiences¿especially our auditory sensations¿insist on a description in terms of both time and frequency. In the present paper this point of view is developed in quantitative language. Signals are represented in two dimensions, with time and frequency as co-ordinates. Such two-dimensional representations can be called ¿information diagrams,¿ as areas in them are proportional to the number of independent data which they can convey. This is a consequence of the fact that the frequency of a signal which is not of infinite duration can be defined only with a certain inaccuracy, which is inversely proportional to the duration, and vice versa. This ¿uncertainty relation¿ suggests a new method of description, intermediate between the two extremes of time analysis and spectral analysis. There are certain ¿elementary signals¿ which occupy the smallest possible area in the information diagram. They are harmonic oscillations modulated by a ¿probability pulse.¿ Each elementary signal can be considered as conveying exactly one datum, or one ¿quantum of information.¿ Any signal can be expanded in terms of these by a process which includes time analysis and Fourier analysis as extreme cases. These new methods of analysis, which involve some of the mathematical apparatus of quantum theory, are illustrated by application to some problems of transmission theory, such as direct generation of single sidebands, signals transmitted in minimum time through limited frequency channels, frequency modulation and time-division multiplex telephony.
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Otoliths have traditionally been used to estimate fish age. However, many factors influence changes in otolith shape, so manual classification remains a complicated task. Very recently, statistical learning techniques have been proposed for automating such a process. We propose performing automatic fish age classification using otolith images (in cases in which growth rings are not properly displayed or are unavailable), morphological and statistical feature-extraction methods and multi-class support vector machines. The results of our experiments, in which we classified cod ages from otolith images, demonstrate the effectiveness of the approach.
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In this paper, we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% from support vector machine. We observed that the classification rate is high for a support vector machine classifier compared to self-organizing map-based approach.
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Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.
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Magnetic resonance imaging (MRI) segmentation has been implemented by many clustering techniques, such as k-means, fuzzy c-means (FCM), learning-vector quantization (LVQ) and fuzzy algorithms for LVQ (FALVQ). Although these algorithms have been successful in applying MRI segmentation, obtaining the right number of clusters and adapting to different cluster characteristics are still not satisfactorily addressed. This report proposes an optimization technique, a hierarchical genetic algorithm with a fuzzy learning-vector quantization network (HGALVQ), to segment multi-spectral human-brain MRI. Evaluation of this approach is based on a real case with human-brain MRI of an individual suffering from meningioma. The HGALVQ is verified by the comparison with other popular clustering algorithms such as k-means, FCM, FALVQ, LVQ, and simulated annealing. Experimental results show that HGALVQ not only returns an appropriate number of clusters and also outperforms other methods in specificity.
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In nonparametric classification and regression problems, regularized kernel methods, in particular support vector machines, attract much attention in theoretical and in applied statistics. In an abstract sense, regularized kernel methods (simply called SVMs here) can be seen as regularized M-estimators for a parameter in a (typically infinite dimensional) reproducing kernel Hilbert space. For smooth loss functions, it is shown that the difference between the estimator, i.e.\ the empirical SVM, and the theoretical SVM is asymptotically normal with rate n\sqrt{n}. That is, the standardized difference converges weakly to a Gaussian process in the reproducing kernel Hilbert space. As common in real applications, the choice of the regularization parameter may depend on the data. The proof is done by an application of the functional delta-method and by showing that the SVM-functional is suitably Hadamard-differentiable.
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Data splitting is an important consideration during artificial neural network (ANN) development where hold-out cross-validation is commonly employed to ensure generalization. Even for a moderate sample size, the sampling methodology used for data splitting can have a significant effect on the quality of the subsets used for training, testing and validating an ANN. Poor data splitting can result in inaccurate and highly variable model performance; however, the choice of sampling methodology is rarely given due consideration by ANN modellers. Increased confidence in the sampling is of paramount importance, since the hold-out sampling is generally performed only once during ANN development. This paper considers the variability in the quality of subsets that are obtained using different data splitting approaches. A novel approach to stratified sampling, based on Neyman sampling of the self-organizing map (SOM), is developed, with several guidelines identified for setting the SOM size and sample allocation in order to minimize the bias and variance in the datasets. Using an example ANN function approximation task, the SOM-based approach is evaluated in comparison to random sampling, DUPLEX, systematic stratified sampling, and trial-and-error sampling to minimize the statistical differences between data sets. Of these approaches, DUPLEX is found to provide benchmark performance with good model performance, with no variability. The results show that the SOM-based approach also reliably generates high-quality samples and can therefore be used with greater confidence than other approaches, especially in the case of non-uniform datasets, with the benefit of scalability to perform data splitting on large datasets.
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Deep brain stimulation of the subthalamic nucleus (DBS-STN) is an approved treatment for advanced Parkinson disease (PD) patients; however, there is a need to further evaluate its effect on gait. This study compares logistic regression (LR), probabilistic neural network (PNN) and support vector machine (SVM) classifiers for discriminating between normal and PD subjects in assessing the effects of DBS-STN on ground reaction force (GRF) with and without medication. Gait analysis of 45 subjects (30 normal and 15 PD subjects who underwent bilateral DBS-STN) was performed. PD subjects were assessed under four test conditions: without treatment (mof-sof), with stimulation alone (mof-son), with medication alone (mon-sof), and with medication and stimulation (mon-son). Principal component (PC) analysis was applied to the three components of GRF separately, where six PC scores from vertical, one from anterior-posterior and one from medial-lateral were chosen by the broken stick test. Stepwise LR analysis employed the first two and fifth vertical PC scores as input variables. Using the bootstrap approach to compare model performances for classifying GRF patterns from normal and untreated PD subjects, the first three and the fifth vertical PCs were attained as SVM input variables, while the same ones plus the first anterior-posterior were selected as PNN input variables. PNN performed better than LR and SVM according to area under the receiver operating characteristic curve and the negative likelihood ratio. When evaluating treatment effects, the classifiers indicated that DBS-STN alone was more effective than medication alone, but the greatest improvements occurred with both treatments together.
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A new model for neuro-fuzzy (NF) classification systems is proposed. The motivation is to utilize the feature-wise degree of belonging of patterns to all classes that are obtained through a fuzzification process. A fuzzification process generates a membership matrix having total number of elements equal to the product of the number of features and classes present in the data set. These matrix elements are the input to neural networks. The effectiveness of the proposed model is established with four benchmark data sets (completely labeled) and two remote sensing images (partially labeled). Different performance measures such as misclassification, classification accuracy and kappa index of agreement for completely labeled data sets, and beta index of homogeneity and Davies-Bouldin (DB) index of compactness for remotely sensed images are used for quantitative analysis of results. All these measures supported the superiority of the proposed NF classification model. The proposed model learns well even with a lower percentage of training data that makes the system fast.
Article
A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described in this paper. The procedure is adaptive in that it customizes a training set, by using a 'pruning' strategy, such that the classification is robust against anatomical variability and pathology. Starting from a set of samples generated from prior tissue probability maps (a 'model') in a standard, brain-based coordinate system ('stereotaxic space'), the method first reduces the fraction of incorrectly labeled samples in this set by using a minimum spanning tree graph-theoretic approach. Then, the corrected set of samples is used by a supervised kNN classifier for classifying the entire 3D image. The classification procedure is robust against variability in the image quality through a non-parametric implementation: no assumptions are made about the tissue intensity distributions. The performance of this brain tissue classification procedure is demonstrated through quantitative and qualitative validation experiments on both simulated MRI data (10 subjects) and real MRI data (43 subjects). A significant improvement in output quality was observed on subjects who exhibit morphological deviations from the model due to aging and pathology.
  • C M Bishop
Bishop, C. M., Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Inc., 2006.
Hybrid intelligent techniques for MRI brain images classification
  • E.-S A El-Dahshan
  • T Hosny
  • A.-B M Salem
El-Dahshan, E.-S. A., T. Hosny, and A.-B. M. Salem, "Hybrid intelligent techniques for MRI brain images classification," Digital Signal Processing, Vol. 20, No. 2, pp. 433-441, 2010.
2.45 GHz (Cw) Microwave Irradiation Alters Circadian Organization, Spatial Memory, Dna Structure in the Brain Cells and Blood Cell Counts of Male Mice, Mus Musculus
  • C M Chaturvedi
Chaturvedi, C. M., et al., "2.45 GHz (Cw) Microwave Irradiation Alters Circadian Organization, Spatial Memory, Dna Structure in the Brain Cells and Blood Cell Counts of Male Mice, Mus Musculus," Progress In Electromagnetics Research B, Vol. 29, No. pp. 23-42, 2011.
  • Chim
Chim. Acta Volume 642 (2009) 59-68]," Analytica Chimica Acta, Vol. 658, No. 1, pp. 106-106, 2010.
  • C M Bishop
Bishop, C. M., Pattern Recognition and Machine Learning (Information Science and Statistics): Springer-Verlag New York, Inc., 2006.