Artificial neural network model for effective cancer classification using microarray gene expression data

Microarray gene expression profile shall be exploited for the efficient and effective classification of cancers. This is a computationally challenging task because of large quantity of genes and relatively small amount of experiments in gene expression data. The repercussion of this work is to devise a framework of techniques based on supervised machine learning for discrimination of acute lymphoblastic leukemia and acute myeloid leukemia using microarray gene expression profiles. Artificial neural network (ANN) technique was employed for this classification. Moreover, ANN was compared with other five machine learning techniques. These methods were assessed on eight different classification performance measures. This article reports a significant classification accuracy of 98% using ANN with no error in identification of acute lymphoblastic leukemia and only one error in identification of acute myeloid leukemia on tenfold cross-validation and leave-one-out approach. Furthermore, models were validated on independent test data, and all samples were correctly classified.

     

Local linear wavelet neural network based breast tumor classification using firefly algorithm

Breast cancer is the major cause of cancer deaths in women today and it is the most common type of cancer in women. This paper presents some experiments for classifying breast cancer tumor and proposes the use of firefly algorithm (FA) to improve the performance of Local linear wavelet neural network. This work in fact uses FA to optimize the parameters of local linear wavelet neural network. The experiments were conducted on extracted breast cancer data from University of Winconsin Hospital, Madison. The result has been compared with a wide range of classifiers to evaluate its performance. The evaluations show that the proposed approach is very robust, effective and gives better correct classification as compared to other classifiers.     

基于小波神经网络的医学图像分类方法

为了提高乳腺癌早期诊断的准确率,将小波理论与神经网络理论相结合提出改进的小波神经网络算法。将经过预处理的医学图像提取特征值,然后利用基于改进的小波神经网络算法的分类器对医学图像进行分类。通过实验表明此分类器具有较高的分类精度,是有效和可行的;与单独使用后向传播神经网络算法相比分类效果也得到了改善。     

The skin cancer classification using deep convolutional neural network

Multimedia Tools and Applications - This paper addresses the demand for an intelligent and rapid classification system of skin cancer using contemporary highly-efficient deep convolutional neural...     

基于改进卷积神经网络的多标记分类算法

良好的特征表达是提高模型性能的关键,然而当前在多标记学习领域,特征表达依然采用人工设计的方式,所提取的特征抽象程度不高,包含的可区分性信息不足。针对此问题,提出了基于卷积神经网络的多标记分类模型ML_DCCNN,该模型利用卷积神经网络强大的特征提取能力,自动学习能刻画数据本质的特征。为了解决深度卷积神经网络预测精度高,但训练时间复杂度不低的问题,ML_DCCNN利用迁移学习方法缩减模型的训练时间,同时改进卷积神经网络的全连接层,提出双通道神经元,减少全连接层的参数量。实验表明,与传统的多标记分类算法以及已有的基于深度学习的多标记分类模型相比,ML_DCCNN保持了较高的分类精度并有效地提高了分类效率,具有一定的理论与实际价值。     

Cancer classification by gradient LDA technique using microarray gene expression data

Cancer classification is one of the major applications of the microarray technology. When standard machine learning techniques are applied for cancer classification, they face the small sample size (SSS) problem of gene expression data. The SSS problem is inherited from large dimensionality of the feature space (due to large number of genes) compared to the small number of samples available. In order to overcome the SSS problem, the dimensionality of the feature space is reduced either through feature selection or through feature extraction. Linear discriminant analysis (LDA) is a well-known technique for feature extraction-based dimensionality reduction. However, this technique cannot be applied for cancer classification because of the singularity of the within-class scatter matrix due to the SSS problem. In this paper, we use Gradient LDA technique which avoids the singularity problem associated with the within-class scatter matrix and shown its usefulness for cancer classification. The technique is applied on three gene expression datasets; namely, acute leukemia, small round blue-cell tumour (SRBCT) and lung adenocarcinoma. This technique achieves lower misclassification error as compared to several other previous techniques.     

Gene boosting for cancer classification based on gene expression profiles

Gene selection is one of the important issues for cancer classification based on gene expression profiles. Filter and wrapper approaches are widely used for gene selection, where the former is hard to measure the relationship between genes and the latter requires lots of computation. We present a novel method, called gene boosting, to select relevant gene subsets by integrating filter and wrapper approaches. It repeatedly selects a set of top-ranked informative genes by a filtering algorithm with respect to a temporal training dataset constructed according to the classification result for the original training dataset. Empirical results on three microarray benchmark datasets have shown that the proposed method is effective and efficient in finding a relevant and concise gene subset. It achieved competitive performance with fewer genes in a reasonable time, as well as led to the identification of some genes frequently getting selected.     

An efficient document classification model using an improved back propagation neural network and singular value decomposition

This paper proposed a new improved method for back propagation neural network, and used an efficient method to reduce the dimension and improve the performance. The traditional back propagation neural network (BPNN) has the drawbacks of slow learning and is easy to trap into a local minimum, and it will lead to a poor performance and efficiency. In this paper, we propose the learning phase evaluation back propagation neural network (LPEBP) to improve the traditional BPNN. We adopt a singular value decomposition (SVD) technique to reduce the dimension and construct the latent semantics between terms. Experimental results show that the LPEBP is much faster than the traditional BPNN. It also enhances the performance of the traditional BPNN. The SVD technique cannot only greatly reduce the high dimensionality but also enhance the performance. So SVD is to further improve the document classification systems precisely and efficiently.     

基于改进小波神经网络的网络流量预测研究

采用小波神经网络对网络流量数据的时间序列进行建模与预测。针对传统小波神经网络训练算法的不足,提出了自适应量子粒子优化算法——AQPSO,用于训练小波神经网络,优化网络参数,建立基于AQPSO算法优化的小波网络预测模型。实验结果表明,该模型对网络流量的短期预测是有效可行的,并具有良好的收敛性和稳定性。     

Power signal classification using dynamic wavelet network

A new approach to classification of non-stationary power signals based on dynamic wavelet has been considered. This paper proposes a model for non-stationary power signal disturbance classification using dynamic wavelet networks (DWN). A DWN is a combination of two sub-networks consisting of a wavelet layer and adaptive probabilistic network. The DWN has the capability of automatic adjustment of learning cycles for different classes of signals, for minimizing error. DWN models are specifically suitable for application in dynamic environments with time varying non-stationary power signals. The test results showed accurate classification, fast and adaptive learning mechanism, fast processing time and overall model effectiveness in classifying various non-stationary power signals. The classification result of the DWN has been compared with that of the probabilistic neural network (PNN).     

Respiratory sound classification by using an incremental supervised neural network

Determination of lung condition by auscultation is a difficult task and requires special training of medical staff. It is, however, a difficult skill to acquire. In decision making, it is significant to analyze respiratory sounds by an algorithm to give support to medical doctors. In this study, first, a rectangular window is formed so that one cycle of respiratory sound (RS) is contained in this window. Then, the windowed time samples are normalized. In order to extract the features, the normalized RS signal is partitioned into 64 samples of long segments. The power spectrum of each segment is computed, and synchronized summation of power spectra components is performed. Feature vectors are formed by the averaged power spectrum components, yielding 32-dimensional vectors. In the study, classification performances of multi-layer perceptron (MLP), grow and learn (GAL) network and a novel incremental supervised neural network (ISNN) are comparatively examined for the classification of nine different RS classes: Bronchial sounds, bronchovesicular sounds, vesicular sounds, crackles sounds, wheezes sounds, stridor sounds, grunting sounds, squawk sounds, and sounds of friction rub.     

Impact of different saturation encoding modes on object classification using a BP wavelet neural network

Wavelet neural networks have been successfully applied to object classification due to their unique various advantages. The wavelet neural network used in this paper is a type of back-propagation algorithm-learning wavelet neural network. The log-sigmoid function and wavelet basis function satisfying the frame condition are employed as an activation function in the output and hidden layers, respectively, and the entropy error function is also used to accelerate the learning speed. The log-sigmoid function has two saturated values, 0 and 1, which are the value of the function at a point whose value changes slightly as the independent variable changes at a somewhat wide range. Using this property of the saturated values and simplifying the mathematical model of neural network classification, we may mathematically prove that using different saturated values to encode the modes can affect the training error, generalization ability, and anti-noise ability of the wavelet neural network, in turn resulting in differences in classification accuracy. The saturated and unsaturated value-encoding modes will both decrease the generalization ability of the network and reduce the classification accuracy due to excessively strong or weak anti-noise ability. Therefore, we propose a type of moderate saturated-value encoding mode, in which the anti-noise ability, the gradient, and error in training process are more moderate than the other two encodings, so that this kind of encoding mode can facilitate a stronger generalization ability and higher classification accuracy for the wavelet neural network, and which have been affirmed in the classification experiments of CHRIS remote-sensing imagery of the Huanghe estuary coastal wetland and SIR-C remote-sensing image of sea ice in the Labrador Gulf, and reaffirmed in classification experiments where noise was added to the test data.     

Short-term traffic flow prediction based on improved wavelet neural network

Neural Computing and Applications - Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow data are difficult to predict accurately. The purpose of this...     

一种基于改进遗传算法的径向基小波神经网络

为了提高神经网络进行函数拟合的精度,首先在三层径向基神经网络基础上通过增加网络层次和改变激励函数提出了一种四层径向基小波神经网络,并采用遗传算法来确定初始网络参数;其次针对遗传算法中容易早熟的缺点,在遗传算法中引入动态平衡策略,根据适应度的变化来动态改变遗传算法中交叉和变异概率,从而增加算法全局探索和局部开发的平衡能力;最后通过对函数拟合试验并与其他方法相比较表明了算法的有效性。     

SVM-ABC based cancer microarray (gene expression) hybrid method for data classification

Microarray technology presents a challenge due to the large dimensionality of the data, which can be difficult to interpret. To address this challenge, the article proposes a feature extraction-based cancer classification technique coupled with artificial bee colony optimization (ABC) algorithm. The ABC-support vector machine (SVM) method is used to classify the lung cancer datasets and compared them with existing techniques in terms of precision, recall, F-measure, and accuracy. The proposed ABC-SVM has the advantage of dealing with complex nonlinear data, providing good flexibility. Simulation analysis was conducted with 30% of the data reserved for testing the proposed method. The results indicate that the proposed attribute classification technique, which uses fewer genes, performs better than other modalities. The classifiers, such as naïve Bayes, multi-class SVM, and linear discriminant analysis, were also compared and the proposed method outperformed these classifiers and state-of-the-art techniques. Overall, this study demonstrates the potential of using intelligent algorithms and feature extraction techniques to improve the accuracy of cancer diagnosis using microarray gene expression data.     

改进的概率神经网络的语义区域分类

针对彩色图像信息量大,分割效果自适应性差的问题,对图像语义区域的分割精度进行控制,提取图像的纹理特征值,再通过改进后的概率神经网络模型对测试样本做分类测试,达到提高图像语义提取和分类准确性的目的。实验表明,改进后的概率神经网络对彩色图像语义区域分类的正确性由原先的70%提高到90%,具有较好的分类效果。     

Blue whale calls classification using short-time Fourier and wavelet packet transforms and artificial neural network

Two new characterization methods based on the short-time Fourier and the wavelet packet transforms are proposed to classify blue whale calls. The vocalizations are divided into short-time overlapping segments before applying these transforms to each segment. Then, the feature vectors are constructed by computing the coefficient energies within two subbands in order to capture the AB phrase and D vocalization characteristics, respectively. Finally, a multilayer perceptron (MLP) is used to classify the vocalization into A, B and D classes. The proposed methods present high classification performance (86.25%) on the tested database.     

Distributed cooperative learning algorithms using wavelet neural network

This paper investigates the distributed cooperative learning (DCL) problems over networks, where each node only has access to its own data generated by the unknown pattern (map or function) uniformly, and all nodes cooperatively learn the pattern by exchanging local information with their neighboring nodes. These problems cannot be solved by using traditional centralized algorithms. To solve these problems, two novel DCL algorithms using wavelet neural networks are proposed, including continuous-time DCL (CT-DCL) algorithm and discrete-time DCL (DT-DCL) algorithm. Combining the characteristics of neural networks with the properties of the wavelet approximation, the wavelet series are used to approximate the unknown pattern. The DCL algorithms are used to train the optimal weight coefficient matrix of wavelet series. Moreover, the convergence of the proposed algorithms is guaranteed by using the Lyapunov method. Compared with existing distributed optimization strategies such as distributed average consensus (DAC) and alternating direction method of multipliers (ADMM), our DT-DCL algorithm requires less information communications and training time than ADMM strategy. In addition, it achieves higher accuracy than DAC strategy when the network consists of large amounts of nodes. Moreover, the proposed CT-DCL algorithm using a proper step size is more accurate than the DT-DCL algorithm if the training time is not considered. Several illustrative examples are presented to show the efficiencies and advantages of the proposed algorithms.

     

Probabilistic neural network for breast cancer classification

Among cancers, breast cancer causes second most number of deaths in women. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis systems have been proposed in the last years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead. In clinical diagnosis, the use of artificial intelligent techniques as neural networks has shown great potential in this field. In this paper, three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer. Decision making is performed in two stages: training the classifiers with features from Wisconsin Breast Cancer database and then testing. The performance of the proposed structure is evaluated in terms of sensitivity, specificity, accuracy and ROC. The results revealed that PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively. MLP was ranked as the second classifier and was capable of achieving 97.80 and 96.34 % classification accuracy for training and validation phases, respectively, using scaled conjugate gradient learning algorithm. However, RBF performed better than MLP in the training phase, and it has achieved the lowest accuracy in the validation phase.     

A dyadic multi-resolution deep convolutional neural wavelet network for image classification

For almost the past four decades, image classification has gained a lot of attention in the field of pattern recognition due to its application in various fields. Given its importance, several approaches have been proposed up to now. In this paper, we will present a dyadic multi-resolution deep convolutional neural wavelets’ network approach for image classification. This approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a Deep Convolutional Neural Wavelet Network (DCNWN). This network is based on the Neural Network (NN) architecture, the Fast Wavelet Transform (FWT) and the Adaboost algorithm. It consists, first, of extracting features using the FWT based on the Multi-Resolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Second, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. Third, we create an AutoEncoder (AE) using wavelet networks of all images. Finally, we apply a pooling for each hidden layer of the wavelet network to obtain a DCNWN that permits the classification of one class and rejects all other classes of the dataset. Classification rates given by our approach show a clear improvement compared to those cited in this article.