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Background Basal cell carcinoma ( BCC) is the most commonly diagnosed cancer in the USA. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including evolving artificial neural networks ( EANNs) and evolving artificial neural network ensembles. Results Experiment results based on 10-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process. [ABSTRACT FROM AUTHOR]

The article discusses the techniques used in locating a specific type of image or file in the U.S. Because of the existence of different types of images which varies in color, type and description, it is difficult for user to locate a specific image that he wants. Previously, the Library of Congress uses Thesaurus for Graphic Materials I and Thesaurus for Graphic Materials II in cataloguing and retrieving images. With the onset of new technology, collaborative tagging is used in assigning keywords to textual documents and image resources. Such a tool is used by sharing sites such as Flick because it allows massive image storage and web diffusion wherein users upload their own images and index them using their own tags.