**7. Conclusions**

In this paper, an integrated decision support system is proposed for the automated mass detection, classification and retrieval of mammograms. The system is evaluated for the retrieval and classification of the mammographic images. The experimental results indicate that the approach is effective to retrieve visually similar lesions from a database and to predict the categories of images for diagnostic correctness. The main objective of this paper is to demonstrate how the image retrieval and classification can be integrated and effectively utilized as a diagnostic support tool to help the radiologist for the mass detection. However, it is recognized that many other advanced image-based features and features from other sources would be necessary for a complete decision support system. In future, we plan to incorporate more advanced features related to the diagnostic relevance into our system and experiment with other classification and combination techniques as well. However, the presence of an expert radiologist is still considered necessary for the overall visual assessment of the breast mass and the final diagnosis, based on the objective evaluation suggested by the system and contextual information from the patient data.
