首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献,搜索用时 15 毫秒
1.
The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. These techniques can help doctors form a second opinion and make a better diagnosis. In this paper we present a novel improvement in neural network training for pattern classification. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon’s information theory. During the training phase the Artificial metaplasticity Multilayer Perceptron (AMMLP) algorithm gives priority to updating the weights for the less frequent activations over the more frequent ones. In this way metaplasticity is modeled artificially. AMMLP achieves a more effcient training, while maintaining MLP performance. To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). AMMLP performance is tested using classification accuracy, sensitivity and specificity analysis, and confusion matrix. The obtained AMMLP classification accuracy of 99.26%, a very promising result compared to the Backpropagation Algorithm (BPA) and recent classification techniques applied to the same database.  相似文献   

2.
Yu  Xiangchun  Chen  Hechang  Liang  Miaomiao  Xu  Qing  He  Lifang 《Multimedia Tools and Applications》2022,81(9):11949-11963
Multimedia Tools and Applications - To train a convolutional neural network (CNN) from scratch is not suitable for medical image tasks with insufficient data. Benefiting from the transfer learning,...  相似文献   

3.
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...  相似文献   

4.
A new learning technique for local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of Error Back propagation and Recursive Least Square (RLS) is introduced for training the parameters of LLWNN. The variance and centers of LLWNN are updated using back propagation and weights are updated using Recursive Least Square (RLS). Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification.  相似文献   

5.
Support-vector-based fuzzy neural network for pattern classification   总被引:3,自引:0,他引:3  
Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network (SVFNN) is proposed for pattern classification in this paper. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. A learning algorithm consisting of three learning phases is developed to construct the SVFNN and train its parameters. In the first phase, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the second phase, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. In the third phase, the relevant fuzzy rules are selected by the proposed reducing fuzzy rule method. To investigate the effectiveness of the proposed SVFNN classification, it is applied to the Iris, Vehicle, Dna, Satimage, Ijcnn1 datasets from the UCI Repository, Statlog collection and IJCNN challenge 2001, respectively. Experimental results show that the proposed SVFNN for pattern classification can achieve good classification performance with drastically reduced number of fuzzy kernel functions.  相似文献   

6.
Chen  Guoming  Chen  Qiang  Long  Shun  Zhu  Weiheng  Yuan  Zeduo  Wu  Yilin 《Pattern Analysis & Applications》2023,26(2):655-667
Pattern Analysis and Applications - In this paper we propose two scale-inspired local feature extraction methods based on Quantum Convolutional Neural Network (QCNN) in the Tensorflow quantum...  相似文献   

7.

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.

  相似文献   

8.
Two methods for classification based on the Bayes strategy and nonparametric estimators for probability density functions are reviewed. The two methods are named the probabilistic neural network (PNN) and the polynomial Adaline. Both methods involve one-pass learning algorithms that can be implemented directly in parallel neural network architectures. The performances of the two methods are compared with multipass backpropagation networks, and relative advantages and disadvantages are discussed. PNN and the polynomial Adaline are complementary techniques because they implement the same decision boundaries but have different advantages for applications. PNN is easy to use and is extremely fast for moderate-sized databases. For very large databases and for mature applications in which classification speed is more important than training speed, the polynomial equivalent can be found.  相似文献   

9.
Liu  Caifeng  Feng  Lin  Liu  Guochao  Wang  Huibing  Liu  Shenglan 《Multimedia Tools and Applications》2021,80(5):7313-7331

Music genre classification based on visual representation has been successfully explored over the last years. Recently, there has been increasing interest in attempting convolutional neural networks (CNNs) to achieve the task. However, most of the existing methods employ the mature CNN structures proposed in image recognition without any modification, which results in the learning features that are not adequate for music genre classification. Faced with the challenge of this issue, we fully exploit the low-level information from spectrograms of audio and develop a novel CNN architecture in this paper. The proposed CNN architecture takes the multi-scale time-frequency information into considerations, which transfers more suitable semantic features for the decision-making layer to discriminate the genre of the unknown music clip. The experiments are evaluated on the benchmark datasets including GTZAN, Ballroom, and Extended Ballroom. The experimental results show that the proposed method can achieve 93.9%, 96.7%, 97.2% classification accuracies respectively, which to the best of our knowledge, are the best results on these public datasets so far. It is notable that the trained model by our proposed network possesses tiny size, only 0.18M, which can be applied in mobile phones or other devices with limited computational resources. Codes and model will be available at https://github.com/CaifengLiu/music-genre-classification.

  相似文献   

10.
A new multilayer incremental neural network (MINN) architecture and its performance in classification of biomedical images is discussed. The MINN consists of an input layer, two hidden layers and an output layer. The first stage between the input and first hidden layer consists of perceptrons. The number of perceptrons and their weights are determined by defining a fitness function which is maximized by the genetic algorithm (GA). The second stage involves feature vectors which are the codewords obtained automaticaly after learning the first stage. The last stage consists of OR gates which combine the nodes of the second hidden layer representing the same class. The comparative performance results of the MINN and the backpropagation (BP) network indicates that the MINN results in faster learning, much simpler network and equal or better classification performance.  相似文献   

11.
Neural Computing and Applications - In this article, a minimum neural network topology in terms of units and connections (neurons and weights), making visual aesthetically categorized images, will...  相似文献   

12.
With the rise of deep neural network, convolutional neural networks show superior performances on many different computer vision recognition tasks. The convolution is used as one of the most efficient ways for extracting the details features of an image, while the deconvolution is mostly used for semantic segmentation and significance detection to obtain the contour information of the image and rarely used for image classification. In this paper, we propose a novel network named bi-branch deconvolution-based convolutional neural network (BB-deconvNet), which is constructed by mainly stacking a proposed simple module named Zoom. The Zoom module has two branches to extract multi-scale features from the same feature map. Especially, the deconvolution is borrowed to one of the branches, which can provide distinct features differently from regular convolution through the zoom of learned feature maps. To verify the effectiveness of the proposed network, we conduct several experiments on three object classification benchmarks (CIFAR-10, CIFAR-100, SVHN). The BB-deconvNet shows encouraging performances compared with other state-of-the-art deep CNNs.  相似文献   

13.
Multimedia Tools and Applications - Breast tumor is one of the major cause of death among women all over the world. Ultrasound imaging-based breast abnormality detection and classification play a...  相似文献   

14.
In this article, an iterative procedure is proposed for the training process of the probabilistic neural network (PNN). In each stage of this procedure, the Q(0)-learning algorithm is utilized for the adaptation of PNN smoothing parameter (σ). Four classes of PNN models are regarded in this study. In the case of the first, simplest model, the smoothing parameter takes the form of a scalar; for the second model, σ is a vector whose elements are computed with respect to the class index; the third considered model has the smoothing parameter vector for which all components are determined depending on each input attribute; finally, the last and the most complex of the analyzed networks, uses the matrix of smoothing parameters where each element is dependent on both class and input feature index. The main idea of the presented approach is based on the appropriate update of the smoothing parameter values according to the Q(0)-learning algorithm. The proposed procedure is verified on six repository data sets. The prediction ability of the algorithm is assessed by computing the test accuracy on 10 %, 20 %, 30 %, and 40 % of examples drawn randomly from each input data set. The results are compared with the test accuracy obtained by PNN trained using the conjugate gradient procedure, support vector machine algorithm, gene expression programming classifier, k–Means method, multilayer perceptron, radial basis function neural network and learning vector quantization neural network. It is shown that the presented procedure can be applied to the automatic adaptation of the smoothing parameter of each of the considered PNN models and that this is an alternative training method. PNN trained by the Q(0)-learning based approach constitutes a classifier which can be treated as one of the top models in data classification problems.  相似文献   

15.
This paper examines a number of experimental investigations of neural networks used for the classification of remotely sensed satellite imagery at the Joint Research Centre over a period of five years, and attempts to draw some conclusions about 'best practice' techniques to optimize network training and overall classification performance. The paper examines best practice in such areas as: network architecture selection; use of optimization algorithms; scaling of input data; avoidance of chaos effects; use of enhanced feature sets; and use of hybrid classifier methods. It concludes that a vast body of accumulated experience is now available, and that neural networks can be used reliably and with much confidence for routine operational requirements in remote sensing.  相似文献   

16.
Ma  Yanjuan  Liu  Jinhai  Qu  Fuming  Zhu  Hongfei 《Applied Intelligence》2022,52(1):305-320

Pattern classification is a fundamental problem in many data-driven application domains. New-labeled data refers to the data with the labels that are new and different from source labels. How to learn the new-labeled data is a crucial research in the data classification. In this paper, an evolved fuzzy min-max neural network for new-labeled data classification (FMM-NLA) is proposed. In FMM-NLA, the network can be self-evolved. Unlike the traditional FMM methods, the trained network of FMM-NLA can be expanded when new-labeled data added. FMM-NLA is a continuing-learning method, which can realize the continuing training process without retraining all the data. In order to verify the superiority of the proposed method, benchmark data sets are used. The experimental results show that FMM-NLA is effective in handling new-labeled data. Moreover, the application result on the pipeline defect recognition in depth shows that FMM-NLA is effective in solving the new-labeled defect recognition problem.

  相似文献   

17.
Multi-class classification is one of the major challenges in real world application. Classification algorithms are generally binary in nature and must be extended for multi-class problems. Therefore, in this paper, we proposed an enhanced Genetically Optimized Neural Network (GONN) algorithm, for solving multi-class classification problems. We used a multi-tree GONN representation which integrates multiple GONN trees; each individual is a single GONN classifier. Thus enhanced classifier is an integrated version of individual GONN classifiers for all classes. The integrated version of classifiers is evolved genetically to optimize its architecture for multi-class classification. To demonstrate our results, we had taken seven datasets from UCI Machine Learning repository and compared the classification accuracy and training time of enhanced GONN with classical Koza’s model and classical Back propagation model. Our algorithm gives better classification accuracy of almost 5% and 8% than Koza’s model and Back propagation model respectively even for complex and real multi-class data in lesser amount of time. This enhanced GONN algorithm produces better results than popular classification algorithms like Genetic Algorithm, Support Vector Machine and Neural Network which makes it a good alternative to the well-known machine learning methods for solving multi-class classification problems. Even for datasets containing noise and complex features, the results produced by enhanced GONN is much better than other machine learning algorithms. The proposed enhanced GONN can be applied to expert and intelligent systems for effectively classifying large, complex and noisy real time multi-class data.  相似文献   

18.
王振华 《计算机应用》2011,31(Z2):92-96
针对Hopfield神经网络的自联想特性,提出一种新的带有粒子群优化过程的Hopfield分类算法(PSO-HOP).该算法采用了Blatt-Vergin (BV)学习算法,一定程度上克服了传统Hopfield容量低的特点.与此同时,还提出了先测量后训练的方法来降低算法的复杂度,提高分类效率,并探讨了样本属性以及类标号在Hopfield神经网络的表示方法,使其能够很好地处理空缺值等噪声数据.通过采用离散型粒子群优化算法对Hopfield的拓扑结构进行优化,可以将多余的神经元分配给不同属性,使得属性在分类中的权重发生改变,从而提高分类精度,避免陷入局部最优值.从统计不同属性被分配神经元的次数中,可以反映出不同属性的重要程度.从大量实验结果可以看出,该算法具有较高的鲁棒性和分类准确度.  相似文献   

19.
DSP-based hierarchical neural network modulation signal classification   总被引:2,自引:0,他引:2  
This paper discusses a real-time digital signal processor (DSP)-based hierarchical neural network classifier capable of classifying both analog and digital modulation signals. A high-performance DSP processor, namely the TMS320C6701, is utilized to implement different kinds of classifiers including a hierarchical neural network classifier. A total of 31 statistical signal features are extracted and used to classify 11 modulation signals plus white noise. The modulation signals include CW, AM, FM, SSB, FSK2, FSK4, PSK2, PSK4, OOK, QAM16, and QAM32. A classification hierarchy is introduced and the genetic algorithm is employed to obtain the most effective set of features at each level of the hierarchy. The classification results and the number of operations on the DSP processor indicate the effectiveness of the introduced hierarchical neural network classifier in terms of both classification rate and processing time.  相似文献   

20.
It is significant to build up the risk classification model of cervical cancer for the evaluation of high-risk population. Data were divided into two sub-data, one is model building sub-data, the other is model testing sub-data. By using of artificial neural network (ANN) analysis method (Back Propagation, BP), the risk classification model had been setup. The parameters were listed as following: the data had been treated as normalization, and the level of network was 3, and the number of neural in hidden level was 5, and the transmitting function between input level and hidden level was logsig, and the transmitting function between hidden level and output level was purelin, and the studying method was Levenberg–Marquardt optimizing, and the error parameter eg = 0.09, maximum epochs me = 8000. The model quality was good (sensitivity = 98%, specificity = 97%), and the back calculation fitting result was excellent. The predictive value of 10 unknown data was also good, during which the correct rate of control group was 100%, and that of case group was 80%. Because ANN is with the character of self-organizing, self-learning and self-adapting, the ANN risk classification model is fit for the screening of high-risk population of local cervical cancer, risk evaluation of cervical cancer and the effect evaluation of the prevention method after training the model by new data of some area.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号