Abstract
The presence of microcalcification clusters in digital mammograms is a primary indicator of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper uses a procedure for the classification of microcalcification clusters in mammograms using sequential Difference of Gaussian filters (DoG) and feedforward Neural Networks (NN). Three methods using class separability, forward sequential search and genetic algorithms for feature selection are compared. We found that the use of Genetic Algorithms (GAs) for selecting the features from microcalcifications and microcalcification clusters that will be the inputs of a feedforward Neural Network (NN) results mainly in improvements in overall accuracy, sensitivity and specificity of the classification.
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Hernández-Cisneros, R.R., Terashima-Marín, H., Conant-Pablos, S.E. (2007). Comparison of Class Separability, Forward Sequential Search and Genetic Algorithms for Feature Selection in the Classification of Individual and Clustered Microcalcifications in Digital Mammograms. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_81
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DOI: https://doi.org/10.1007/978-3-540-74260-9_81
Publisher Name: Springer, Berlin, Heidelberg
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