Result: Classification of individual and clustered microcalcifications in digital mammograms using evolutionary neural networks

Title:
Classification of individual and clustered microcalcifications in digital mammograms using evolutionary neural networks
Authors:
HERNANDEZ-CISNEROS, Rolando R, TERASHIMA-MARIN, Hugo
Source:
MICAI 2006 (advances in artificial intelligence)Lecture notes in computer science. :1200-1210
Publisher Information:
Berlin; Heidelberg; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 13 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Homme, Human, Hombre, Ajustement modèle, Model matching, Ajustamiento modelo, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Amas, Cluster, Montón, Classification, Clasificación, Diagnostic, Diagnosis, Diagnóstico, Femme, Woman, Mujer, Filtre, Filter, Filtro, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Maladie, Disease, Enfermedad, Mammographie, Mammography, Mastografía, Microcalcification, Microcalcificación, Mortalité, Mortality, Mortalidad, Processus Gauss, Gaussian process, Proceso Gauss, Réseau neuronal, Neural network, Red neuronal, Rétropropagation, Backpropagation, Retropropagacíon, Spécificité, Specificity, Especificidad, Tumeur maligne, Malignant tumor, Tumor maligno
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Center for Intelligent Systems, Tecnológico de Monterrey, Campus Monterrey Ave. Eugenio Garza Sada 2501 Sur, Monterrey, Nuevo León 64849, Mexico
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19151801
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19151801
Database:
PASCAL Archive

Further Information

Breast cancer is one of the main causes of death in women and early diagnosis is an important means to reduce the mortality rate. The presence of microcalcification clusters are primary indicators of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper proposes a procedure for the classification of microcalcification clusters in mammograms using sequential difference of gaussian filters (DoG) and three evolutionary artificial neural networks (EANNs) compared against a feedforward artificial neural network (ANN) trained with backpropagation. We found that the use of genetic algorithms (GAs) for finding the optimal weight set for an ANN, finding an adequate initial weight set before starting a backpropagation training algorithm and designing its architecture and tuning its parameters, results mainly in improvements in overall accuracy, sensitivity and specificity of an ANN, compared with other networks trained with simple backpropagation.