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- W1976307966 abstract "Neural Networks (NNs) have been efficaciously used for classification purposes in medical domains, including the classification of microcalcifications in digital mammograms. Unfortunately, for a NN to be effective in a particular purview, its architecture, training algorithm and the domain variables selected as inputs must be amply chosen. In this paper, a novel Ant Colony Optimization (ACO) based learning approach with a modified architecture is proposed to speed up the learning phase of a Backpropagation Neural Network (BPN) classifier. The novel ACO simulates the behavior of weaver ants, known for their unique nest building behavior where workers construct nests by weaving together leaves using larval silk. The proposed Weaver Ant Colony Optimization (WACO) based Backpropagation Neural Network (WACO-BPN) is applied for classifying digital mammograms received from MIAS database. The performance is analyzed with Receiver Operating Characteristics (ROC) curve. The greater accuracy of 97% states the grander performance of the proposed neural network learning approach." @default.
- W1976307966 created "2016-06-24" @default.
- W1976307966 creator A5020866279 @default.
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- W1976307966 date "2013-07-01" @default.
- W1976307966 modified "2023-09-25" @default.
- W1976307966 title "Weaver Ant Colony Optimization-Based Neural Network Learning for Mammogram Classification" @default.
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- W1976307966 doi "https://doi.org/10.4018/ijsir.2013070102" @default.
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