El-Sayed A. El-Dahshan

Hybrid intelligent techniques for MRI brain images classification

This paper presents a hybrid technique for the classification of the magnetic resonance images (MRI). The proposed hybrid technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related to MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images have been reduced, using principal component analysis (PCA), to the more essential features. In the classification stage, two classifiers have been developed. The first classifier based on feed forward back-propagation artificial neural network (FP-ANN) and the second classifier is based on k-nearest neighbor (k-NN). The classifiers have been used to classify subjects as normal or abnormal MRI human images. A classification with a success of 97% and 98% has been obtained by FP-ANN and k-NN, respectively. This result shows that the proposed technique is robust and effective compared with other recent work.

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

Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.

Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.

Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network

Pulse-coupled neural networks (PCNNs) are a biologically inspired type of neural networks. It is a simplified model of the cats visual cortex with local connections to other neurons. PCNN has the ability to extract edges, segments and texture information from images. Only a few changes to the PCNN parameters are necessary for effective operation on different types of data. This is an advantage over published image processing algorithms that generally require information about the target before they are effective. The main aim of this paper is to provide an accurate boundary detection algorithm of the prostate ultrasound images to assist radiologists in making their decisions. To increase the contrast of the ultrasound prostate image, the intensity values of the original images were adjusted firstly using the PCNN with median filter. It is followed by the PCNN segmentation algorithm to detect the boundary of the image. Combining adjusting and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. The experimental results obtained show that the overall boundary detection overlap accuracy offered by the employed PCNN approach is high compared with other machine learning techniques including Fuzzy C-mean and Fuzzy Type-II.

GENETIC PROGRAMING MODELING FOR NUCLEUS–NUCLEUS COLLISIONS

High Energy Physics (HEP), due to the vast and complex data expected from current and future experiments, is in need of powerful and efficient techniques for various analysis tasks. Genetic Programing (GP) is a powerful technique that can be used such complex tasks. In this paper, Genetic programing is used for modeling the functions that describe the pseudo-rapidity distribution of the shower particles for 12C, 16O, 28Si and 32S on nuclear emulsion and also to predict the distributions that are not present in the training set and matched them effectively. The proposed method shows a better fitting with experimental data. The GP prediction results prove a strong presence modeling in heavy ion collisions.

Machine learning in electrocardiogram diagnosis

The electrocardiogram (ECG) is a measure of the electrical activity of the heart. Since its introduction in 1887 by Waller, it has been used as a clinical tool for evaluating heart function. A number of cardiovascular diseases (CVDs) (arrhythmia, atrial fibrillation, atrioventricular (AV) dysfunctions, and coronary arterial disease, etc.) can be detected non-invasively using ECG monitoring devices. With the advent of modern signal processing and machine learning techniques, the diagnostic power of the ECG has expanded exponentially. The principal reason for this is the expanded set of features that are typically extracted from the ECG time series. The enhanced feature space provides a wide range of attributes that can be employed in a variety of machine learning techniques, with the goal of providing tools to assist in CVD classification. This paper summarizes some of the principle machine learning approaches to ECG classification, evaluating them in terms of the features they employ, the type(s) of CVD(s) to which they are applied, and their classification accuracy.

Artificial Neural Network And Genetic Algorithm Hybrid Technique For Nucleus–Nucleus Collisions

Selecting the optimal topology of a neural network for a particular application is a difficult task. Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) to calculate the pseudo-rapidity distribution of the shower particles for C12, O16, Si28, and S32 on nuclear emulsion. An efficient NN has been designed by GA to predict the distributions that are not present in the training set and matched them effectively. The proposed method shows a better fitting with experimental data. The hybrid technique GA–ANN simulation results prove a strong presence modeling in heavy ion collisions.

Denoising of Heart Sound Signals Using Discrete Wavelet Transform

Signal denoising remains to be one of the main problems in the field of signal processing. Various signal denoising algorithms using wavelet transforms have been introduced. Wavelets show superior signal denoising performance due to their properties such as multiresolution and windowing. This study focuses on denoising of phonocardiogram (PCG) signals using different families of discrete wavelet transforms, thresholding types and techniques, and signal decomposition levels. In particular, we discuss the effect of the chosen wavelet function and wavelet decomposition level on the efficiency of the denoising algorithm. Denoised signals are compared with the original PCG signal to determine the most suitable parameters (wavelet family, level of decomposition, and thresholding type) for the denoising process. The performance of our algorithm is evaluated using the signal-to-noise ratio, percentage root-mean-square difference, and root-mean-square error. The results show that the level of decomposition and thresholding type are the most important parameters affecting the efficiency of the denoising algorithm. Finally, we compare our results with those from other studies to test and optimize the performance of the proposed algorithm.

Accurate detection of prostate boundary in ultrasound images using biologically-inspired spiking neural network

The main aim of this paper is to provide an accurate boundary detection algorithm of the prostate ultrasound images to assist radiologists in making their decisions. To increase the contrast of the ultrasound prostate image, the intensity values of the original images were adjusted firstly using the PCNN with median filter. It is followed by the PCNN segmentation algorithm to detect the boundary of the image. Combining adjusting and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Analysis and experimental results show that the best segmentation output can be drawn from the simple and sophisticated ultrasound images using the spiking neural networks.

Application of genetic programming for proton-proton interactions

AbstractThe aim of the present work is to use one of the machine learning techniques named the genetic programming (GP) to model the p-p interactions through discovering functions. In our study, GP is used to simulate and predict the multiplicity distribution of charged pions (P(nch)), the average multiplicity (〈nch〉) and the total cross section (σtot) at different values of high energies. We have obtained the multiplicity distribution as a function of the center of mass energy (