Journal of Signal Processing Systems
Integrated Computer-Aided Engineering
Edges Detection of Clusters of Microcalcifications with SOM and Coordinate Logic Filters
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Detection of Microcalcifications Using Coordinate Logic Filters and Artificial Neural Networks
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Twin support vector machines and subspace learning methods for microcalcification clusters detection
Engineering Applications of Artificial Intelligence
An efficient digital mammogram image classification using DTCWT and SVM
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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This paper presents an approach for detecting microcalcification in digital mammograms employing combination of artificial neural networks (ANN) and wavelet-based subband image decomposition. The microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands, suppressing the low-frequency subband, a and finally, reconstructing the mammogram from the subbands containing only high frequencies. We use these results as an input of neural network for classification. The neural network contains one input, two hidden and one output layers. Layers have 30, 45, 20, and 1 neurons respectively The proposed methodology is tested using the Nijmegen and the Mammogr-aphic Image Analysis Society (MIAS) Mammographic databases. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve (Az)