Purpose:To automate the diagnosis of malignancy by classifying breast tissues as negative or positive for malignancy in gadolinium-enhanced dynamic magnetic resonance (MR) images, using static region descriptors and a neural network classifier. Materials and Methods:We propose a novel approach whereby the classifier evaluates a number of parameters that identify important tumor characteristics, as obtained by digital image processing techniques. These parameters include static signal intensity (SI) after contrast enhancement, mass margin descriptors, evaluation of mass shape by calculation of eccentricity, mass size, and mass granularity by texture analysis. Datasets for 14 patients were obtained by use of the 1.5T PMRTOW Clinical Imager.Results: Statistical performance evaluation of the neural networks indicated 90%-100% sensitivity, 91%-100% specificity, and 91%-100% accuracy. Conclusion:Although this work is preliminary, it may reduce overall health-care time and costs, and enable higher accuracy in automated breast cancer detection systems.

Action rules are built from atomic expressions called atomic action terms and they describe possible transitions of objects from one state to another. They involve changes of values within one decision attribute. Association action rule is similar to an action rule but it may refer to changes of values involving several attributes listed in its decision part. Action paths are defined as sequences of association action rules with the assumption that the last rule in a sequence is as action rule. Early research on action rule discovery usually required the extraction of classification rules before constructing any action rule. Newest algorithms discover action rules and association action rules directly from an information system. This paper presents a strategy for generating association action rules and action paths by incorporating the use of meta-actions and influence matrices. Action paths show the cascading effect of meta-actions leading to a desired goal.

Action rules assume that attributes in a database are divided into two groups: stable and flexible. In general, an action rule can be constructed from two rules extracted earlier from the same database. Furthermore, we assume that these two rules describe two different decision classes and our goal is to reclassify objects from one of these classes into the other one. Flexible attributes are essential in achieving that goal because they provide a tool for making hints to a user about what changes within some values of flexible attributes are needed for a given group of objects to reclassify them into a new decision class. A new subclass of attributes called semi-stable attributes is introduced. Semi-stable attributes are typically a function of time and undergo deterministic changes~e.g., attribute age or height!. So, the set of conditional attributes is partitioned into stable, semi-stable, and flexible. Depending on the semantics of attributes, some semi-stable attributes can be treated as flexible and the same new action rules can be constructed. These new action rules are usually built to replace some existing action rules whose confidence is too low to be of any interest to a user. The confidence of new action rules is always higher than the confidence of rules they replace. Additionally, the notion of the cost and feasibility of an action rule is introduced in this article. A heuristic strategy for constructing feasible action rules that have high confidence and possibly the lowest cost is proposed.

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