scholarly journals A Comparative Study on Morphological Neural Networks for Binary Classification

2021 ◽  
Author(s):  
Luana Felipe de Barros ◽  
Marcos Eduardo Valle
Keyword(s):  
Neural Networks ◽  
Comparative Study ◽  
Binary Classification ◽  
Morphological Neural Networks

Redes neurais morfológicas representam uma classe de redes neurais artificiais cujos neurônios efetuam uma operação da morfologia matemática seguida da aplicação de uma função de ativação. Este artigo apresenta um estudo comparativo de diferentes abordagens que utilizam redes neurais morfológicas. Especificamente, de acordo com a regra de treinamento, revisamos abordagens incrementais, baseadas no método de máxima descida, máquinas de aprendizado extremo e procedimento de otimização convexa-côncava. Experimentos computacionais mostraram que, em média, o perceptron erosão-dilatação reduzido com as estratégias bagging e ensemble obteve melhores resultados em diversos problemas de classificação binária.

scholarly journals Bipolar Morphological Neural Networks: Gate-Efficient Architecture for Computer Vision

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Elena E. Limonova ◽  
Daniil M. Alfonso ◽  
Dmitry P. Nikolaev ◽  
Vladimir V. Arlazarov
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Morphological Neural Networks

Entropy estimation of the fragments of chest X-ray images

2021 ◽  
pp. 20-26
Author(s):  
Aleksei Aleksandrovich Rumyantsev ◽  
Farkhad Mansurovich Bikmuratov ◽  
Nikolai Pavlovich Pashin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Binary Classification ◽  
Statistical Characteristics ◽  
Type I ◽  
Deep Convolutional Neural Networks ◽  
Preliminary Calculation ◽  
X Ray ◽  
The Matrix ◽  
Chest X Ray

The subject of this research is medical chest X-ray images. After fundamental pre-processing, the accumulated database of such images can be used for training deep convolutional neural networks that have become one of the most significant innovations in recent years. The trained network carries out preliminary binary classification of the incoming images and serve as an assistant to the radiotherapist. For this purpose, it is necessary to train the neural network to carefully minimize type I and type II errors. Possible approach towards improving the effectiveness of application of neural networks, by the criteria of reducing computational complexity and quality of image classification, is the auxiliary approaches: image pre-processing and preliminary calculation of entropy of the fragments. The article provides the algorithm for X-ray image pre-processing, its fragmentation, and calculation of the entropy of separate fragments. In the course of pre-processing, the region of lungs and spine is selected, which comprises approximately 30-40% of the entire image. Then the image is divided into the matrix of fragments, calculating the entropy of separate fragments in accordance with Shannon’s formula based pm the analysis of individual pixels. Determination of the rate of occurrence of each of the 255 colors allows calculating the total entropy. The use of entropy for detecting pathologies is based on the assumption that its values differ for separate fragments and overall picture of its distribution between the images with the norm and pathologies. The article analyzes the statistical values: standard deviation of error, dispersion. A fully connected neural network is used for determining the patterns in distribution of entropy and its statistical characteristics on various fragments of the chest X-ray image.

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