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This work develops a microcalcifications’ detection system in mammograms by using difference of Gaussians filters (DoG) and artificial neural networks (ANN). The digital image processing proposed show the basic wavelet-based behavior of DoG as a mother function frequently used in several vision tasks, and in this case, used in order to enhance the microcalcifications’ traces in standard mammograms and further to achieve its detection via ANN. In order to achieve this, a segmentation algorithm is implemented for reaching a threshold in already processed images, and finally, the resultant information is given to the ANN. The neural network used to perform the detection is a hybrid feedforward-Kohonen one, implemented with a hard-limit transfer function in the first layer and a self-organizing map (SOM) responsible for microcalcifications’ topologic adjustment in the second layer. Basically, this clustering method gave us a robust solution of the problem and the detection was performed efficiently. There are no considerations relative to morphologic analysis of microcalcifications for diagnosis in this work.