Alexis Marcano-Cedeño

WBCD breast cancer database classification applying artificial metaplasticity neural network

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 Shannons 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.

Feature selection using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network

The feature selection has been widely used to reduce the data dimensionality. Data reduction improve the classification performance, the approximation function, and pattern recognition systems in terms of speed, accuracy and simplicity. A strategy to reduce the number of features in local search are the sequential search algorithms. In this work is presented a feature selection method based on Sequential Forward Selection (SFS) and Feed Forward Neural Network (FFNN) to estimate the prediction error as a selection criterion. Three well-known database have been used to test the SFS-FFNN with Artificial Metaplasticity on Perceptron Multilayer (AMMLP). The AMMLP is a new method applied for classification of patterns. The results obtained by SFS-FFNN with AMMLP in classification accuracy are superior than obtained by conventional BP algorithm and other recent feature selection algorithms applied to the same database. By these reasons the proposed method SFS-FFNN with AMMLP is an interesting alternative to reduce the data dimensionality and provide a high accuracy.

Breast cancer classification applying artificial metaplasticity algorithm

A novel improvement in neural network training for pattern classification is presented in this paper. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannons information theory. This algorithm is applicable to artificial neural networks (ANNs) in general, although here it is applied to a multilayer perceptron (MLP). During the training phase, the artificial metaplasticity multilayer perceptron (AMMLP) algorithm assigns higher values for updating the weights in the less frequent activations than in the more frequent ones. AMMLP achieves a more efficient training and improves MLP performance. The well-known and readily available Wisconsin Breast Cancer Database (WBCD) has been used to test the algorithm. Performance of the AMMLP was tested through classification accuracy, sensitivity and specificity analysis, and confusion matrix analysis. The results obtained by AMMLP are compared with the backpropagation algorithm (BPA) and other recent classification techniques applied to the same database. The best result obtained so far with the AMMLP algorithm is 99.63%.

Wood defects classification using Artificial Metaplasticity neural network

Artificial Metaplasticity (AMP) is a novel Artificial Neural Network (ANN) training algorithm inspired in biological metaplasticity property of neurons and Shannons information theory. During training phase, the AMP training algorithm gives more relevance to the less frequent patterns and subtracts relevance to the frequent ones, achieving a much more efficient training, while at least maintaining the MLPs performance. AMP is specially recommended when few patterns are available to train the network. In this paper, we implement an Artificial Metaplasticity MLP (AMMLP) in order to classify defects in wood images. The defects are three different types of knots found in wood surfaces. Classification is based on the features obtained from Gabor filters. Experimental results show that AMMLPs reach better accuracy than the classical BP algorithm as well as with recently proposed algorithms applied on the same database.

Breast Cancer Classification Applying Artificial Metaplasticity

In this paper we are apply Artificial Metaplasticity MLP (MMLPs) to Breast Cancer Classification. Artificial Metaplasticity is a novel ANN training algorithm that gives more relevance to less frequent training patterns and subtract relevance to the frequent ones during training phase, achieving a much more efficient training, while at least maintaining the Multilayer Perceptron performance. Wisconsin Breast Cancer Database (WBCD) was used to train and test MMLPs. WBCD is a well-used database in machine learning, neural networks and signal processing. Experimental results show that MMLPs reach better accuracy than any other recent results.

Artificial metaplasticity MLP applied to image classification

In this paper we apply Artificial Metaplasticity to a Multilayer Perceptron (MLP) for image classification. Artificial Metaplasticity is a novel Artificial Neural Network (ANN) training algorithm that gives more relevance to less frequent training patterns and subtracts relevance to the frequent ones during training phase, achieving a much more efficient training, while at least maintaining the MLP performance. In this paper, we test Metaplasticity MLP (MMLP) algorithm on an image standard data set: theWisconsin Breast Cancer Database (WBCD). WBCD is a well-used database in Machine Learning, ANN and Signal Processing. Experimental results show that MMLPs reach better accuracy than any other recent results.

Application of Artificial Metaplasticity Neural Networks to Cardiac Arrhythmias Classification

Correct diagnosis of cardiac arrhythmias is one of the major problems in medical field. Cardiac arrhythmias can be early detected and diagnosed to prevent the occurrence of heart attack as well as the consequent deaths. An effective method for early detection of these arrhythmias, and thus to procure early treatment, is necessary. In this research we have applied artificial metaplasticity multilayer perceptron (AMMLP) to cardiac arrhythmias classification. The MIT-BIH Arrhythmia Database was used to train and test AMMLPs. The obtained AMMLP classification accuracy of 98.25%, is an excellent result compared to the classical MLP and recent classification techniques applied to the same database.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection.

A prediction model to diabetes using artificial metaplasticity

Diabetes is the most common disease nowadays in all populations and in all age groups. Different techniques of artificial intelligence has been applied to diabetes problem. This research proposed the artificial metaplasticity on multilayer perceptron (AMMLP) as prediction model for prediction of diabetes. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with other algorithms, recently proposed by other researchers, that were applied to the same database. The best result obtained so far with the AMMLP algorithm is 89.93%.

Testing artificial metaplasticity in MLP applications

In this work we tested and compared artificial metaplasticity (AMP) results for multilayer perceptrons (MLPs). AMP is a novel artificial neural network (ANN) training algorithm inspired on the biological metaplasticity property of neurons and Shannons information theory. During training phase, AMP training algorithm gives more relevance to less frequent patterns and subtracts relevance to the frequent ones, claiming to achieve a much more efficient training, while at least maintaining the MLP performance. AMP is specially recommended when few patterns are available to train the network. We implement an artificial metaplasticity MLP (AMMLP) on standard and well-used databases for machine learning. Experimental results show the superiority of AMMLPs when compared with recent results on the same databases.