A computer-aided method for detecting, classifying, and displaying candidate abnormalities, such as microcalcifications and interstitial lung disease in digitized medical images, such as mammograms and chest radiographs, a computer programmed to implement the method, and a data structure for storing required parameters, wherein in the classifying method candidate abnormalities in a digitized medical image are located, regions are generated around one or more of the located candidate abnormalities, features are extracted from at least one of the located candidate abnormalities within the region and from the region itself, the extracted features are applied to a classification technique, such as an artificial neural network (ANN) to produce a classification result (i.e., probability of malignancy in the form of a number and a bar graph), and the classification result is displayed along with the digitized medical image annotated with the region and the candidate abnormalities within the region. In the detecting method candidate abnormalities in each of a plurality of digitized medical images are located, regions around one or more of the located candidate abnormalities in each of a plurality of digitized medical images are generated, the plurality of digitized medical images annotated with respective regions and candidate abnormalities within the regions are displayed, and a first indicator (e.g., blue arrow) is superimposed over candidate abnormalities comprising of clusters and a second indicator (e.g., red arrow) is superimposed over candidate abnormalities comprising of masses. In a user modification mode, during classification, a user modifies the located candidate abnormalities, the determined regions, and/or the extracted features, so as to modify the extracted features applied to the classification technique and the displayed results, and, during detection, a user modifies the located candidate abnormalities, the determined regions, and the extracted features, so as to modify the displayed results.
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